MRes students work on their research project throughout the year. You can apply for one of the projects listed below, or contact your preferred supervisor to discuss a different project.
You must name at least one potential supervisor in your personal statement when you apply.
Applications will be considered in three rounds. We encourage you to apply in Round 2 or 3. If you are applying in round 4, some projects may have already been allocated so please consider including a second or third choice project in your application.
Visit our How do I apply? page for full details of the application process including deadlines.
Projects available for 2027-28 entry
- Dr Amy Howard
- Dr Andriy Kozlov
- Professor Anil Bharath
- Dr Amanda Foust
- Dr Chiu Fan Lee
- Dr Chris Richards
- Dr Chris Rowlands
- Dr Dandan Zhang
- Dr Guang Yang
- Dr Huai-Ti Lin
- Dr Jo Jackson
- Dr Jun Ishihara
- Dr Kaushik Jayaram
- Dr Laki Pantazis
- Dr Swathi Padmanabhan
- Professor Dario Farina
- Dr David Labonte
- Dr Hayriye Cagnan
- Professor Darryl Overby
- Dr James Choi
- Professor Martyn Boutelle
- Professor Mengxing Tang
- Professor Holger Krapp
- Professor Julien Vermot
- Professor Manos Drakakis
- Professor Reiko Tanaka
- Professor Rylie Green
- Dr Sophie Morse
Dr Amy Howard
Profile: https://profiles.imperial.ac.uk/a.howard
Contact details: a.howard@imperial.ac.uk
| Project title | Description |
| Mapping Brain Connectivity Via Low-Cost 3D Polarised Light Imaging | Polarised light imaging (PLI) is a powerful microscopy method for ex vivo investigations of brain connectivity ("the structural connectome"). PLI utilises the optical property of birefringence within brain tissue to estimate axonal orientations within brain tissue with micron-scale resolutions. However, most PLI systems can only reliably inform on axon orientations within the 2D microscopy plane. This is problematic as it limits the utility of 2D PLI for imaging the 3D trajectories of axons linking different brain regions. Extracting 3D orientations typically requires bespoke set-ups which are expensive, limiting wide-spread access. This project aims develop a low-cost 3D polarised light imaging system for accessible, high-resolution connectomics. This will include microscope development, data acqusition using postmortem brain samples, image analysis and connectivity mapping. Investigations could consider whole-brain connectomics, or focus on specific structures such as the hippocampus which has key functions in memory and learning, and is implicated in conditions such as Alzheimer's disease. Comparisons with diffusion MRI acquired in the same tissue can be used to validate a drive methods for estimating brain connectivity in vivo. |
| Can Combined MRI-PLI Analysis Provide Reliable Myelin Estimates? | Myelin, the insulating sheath surrounding axons in the brain, is crucial for the efficient transmission of electrical signals in the nervous system, enabling faster communication between neurons and supporting overall brain and spinal cord function. Reliable myelin imaging is therefore essential to the diagnosis of different demyelinating pathologies such as multiple sclerosis. Polarised light imaging (PLI) - a microscopy method sensitive to myelinated axons in the brain - uses the optical property of tissue birefringence to inform on axonal orientations in ex vivo brain tissue samples. One of the signals from PLI, the tissue retardance, is dependent simultaneously on both the 3D orientation of axons and the amount of myelin in the tissue. Without the use of bespoke set-ups, these two signals are difficult to disentangle, making robust analysis of either property (the 3D orientation or amount of myelin) ill-posed. This project will aim to build on the combined analysis of MRI and PLI data to develop a robust method to simultanously estimate both the axons orientation and its degree of myelination. These data can provide insight on the fundamental understanding of how myelin varies across different pathways in the brain, and the preferential dyemyelination of specific white matter bundles in pathological conditions. Comparisons with myelin-sensitive MRI in the same samples will ellicuidate the extent to which demyelinating pathologies can be detected in vivo. |
| Computational Neuroanatomy: Machine Learning for Microscopy in the Brain | The brain’s microstructure is remarkably rich and diverse — from the layered organisation of the cortex to intricate bundles of axons weaving through white matter. These features are vividly captured in high-resolution microscopy images, revealing striking regional differences in cellular and fibre architecture. In this project, you will use image analysis and machine or deep learning techniques to extract meaningful biological information from microscopy data and map microstructural variation across the brain. You will have access to detailed stained histological sections highlighting cell bodies and axonal architecture, as well as polarised light imaging (PLI) data that describes fibre orientation in fine detail. Students are welcome to propose their own ideas, but possible project directions include: - Cell segmentation and classification of cell types - Identification of cortical layers - Multi-modal brain parcellation or region classification - Extraction of spatial gradients across the brain - Comparison with MRI data acquired in the same brains - Cross-species analysis to explore similarities and differences across rodents, monkeys, and humans This project offers an opportunity to apply and expand skills in image analysis, machine learning, and neuroscience. It is well suited to students interested in medical imaging, neuroscience, and machine learning. |
| From MRI to Microns: Mapping Brain Gradients Across Scales | How do microstructural features of the brain — like cell density, laminar organisation, or fibre architecture — relate to large-scale organisation seen in MRI? In this project, you will explore this question by extracting microstructural gradients from microscopy data and comparing them to macro-scale gradients derived from MRI, all within the same brain. You’ll work with high-resolution histological data (e.g. Nissl, myelin, PLI) alongside co-registered MRI (e.g. T1, diffusion), using dimensionality reduction techniques (e.g. diffusion embedding, PCA) to generate and compare gradients across modalities. The aim is to improve ultimately in vivo human MRI analysis by providing biological validation for MRI-derived gradients, informing more anatomically grounded brain parcellations, and supporting the development of imaging biomarkers linked to underlying tissue properties. The project is well suited to students interested in multiscale brain mapping, medical imaging, and computational neuroscience. |
| Tracking Connections: Better Methods for MRI Structural Brain Mapping | Mapping how the brain is structurally connected is central to human neuroscience, yet our abilities to achieve this in living people (via MRI tractography) remains limited in accuracy, particularly in complex regions such as subcortical structures, crossing fibres, and cortical entry zones. This project will explore new tractography methods using multi-modal MRI and/or the incorporation of high-resolution microscopy data (e.g. Nissl, myelin stains, polarised light imaging) for model development or validation. Depending on interests, the project can focus purely on MRI-based approaches, or expand to multimodal analysis combining MRI and microscopy. Possible directions include: - Developing new fibre tracking algorithms using machine or deep learning (e.g. reinforcement learning) - Designing tractography methods specialised for subcortical or superficial white matter regions - Mapping cortical fibre fanning and entry patterns, using microscopy for validation or model inspiration - Creating high-resolution tract reconstructions in bespoke datasets and translating these methods to standard human MRI data Our aim will be to develop and evaluate methods that ultimately benefit in vivo human connectivity mapping by improving orientation estimation, resolving complex white matter configurations, and incorporating biologically informed constraints derived from MRI and/or microscopy. This project is well suited for students interested in medical image analysis, brain connectivity, machine learning, and neuroanatomy. |
| Redundancy or Specialisation? Linking Brain Connectivity to Microstructure | Understanding how different brain regions are connected — and how their microstructure supports these connections — is a key challenge in neuroscience and neuroengineering. This project will explore whether structurally connected brain regions are also microstructurally similar, and how this relationship varies across functional networks in the brain. You will use a unique multimodal dataset that pairs diffusion MRI (which maps structural connections) with high-resolution histology (e.g. Nissl and myelin stains, polarised light imaging) that captures microstructural features such as cell density, layer thickness, and fibre orientation. The aim is to develop a quantitative analysis framework that: - Measures microstructural similarity between brain regions - Links this to connectivity strength and distance - Tests whether similarity is greater within networks (supporting redundancy), or systematically different between networks (supporting computational specialisation) You may also explore whether these patterns follow smooth microstructural gradients across the brain or reflect discrete network boundaries. This project will involve biomedical image analysis, feature extraction from multimodal imaging, and applying computational models to study brain structure–function relationships. It is suited to students interested in neuroimaging and computational neuroscience. |
| Biophysical Modelling of Tissue Microstructure with MRI | MRI — and especially diffusion MRI — lets us explore the microstructure of tissue, revealing details like cell shape, size, and composition without the need for invasive procedures. To interpret these signals, we use biophysical models: mathematical representations of how water moves through brain and body tissue.By modelling different tissue types with simplified geometries (for example, “sticks†for axons in white matter or “spheres†for cell bodies in grey matter), we can predict the MRI signal and estimate key features such as axon density, fibre orientation, or cell body size. These models act as a kind of in vivo microscope, offering insight into the living brain and body. They also provide a powerful tool for detecting and monitoring changes caused by cancer, disease, or injury. Students are welcome to propose projects to: - Develop or extend models to represent specific disease-related changes in brain tissue - Optimise MRI acquisition protocols to better measure particular features - Design and implement novel model fitting strategies to extract useful parameters from data - Build digital twins of brain tissue, using Monte Carlo simulations to track how water molecules move through realistic 3D meshes, providing a more flexible alternative to simplified equations This project is well suited for students interested in biomedical imaging, mathematical modelling, and applying engineering methods to brain health. Depending on the students interests, the project can lean more towards theory, simulation, data analysis, or experimental design. |
| High-Resolution Mapping and Modelling of Hippocampal Connectivity | The hippocampus is a critical brain structure involved in memory and navigation, but its internal connectivity is challenging to capture with standard MRI due to its small size and complex organisation. In this project, you will develop methods for high-resolution mapping of hippocampal connectivity, using data from either high-resolution diffusion and structural MRI, and/or microscopy. The aim is to explore how fibre pathways and subfields within the hippocampus can be mapped more accurately at high resolution, and develop a model to translate this detailed information to lower-resolution, standard MRI datasets. This approach aims to make insights gained from specialised datasets applicable to broader populations or clinical settings. Depending on interests, the project may include: - Analysing high-resolution MRI or microscopy data to extract detailed hippocampal connectivity - Combining structural and diffusion MRI for more accurate mapping - Building a model to infer fine-scale connectivity from standard MRI inputs - Testing the method on publicly available human datasets (e.g. HCP) This project is ideal for students interested in neuroimaging, neuroanatomy, data fusion and computational modelling. |
| Building a Toolbox for Robust Quantitative MRI | Quantitative MRI aims to go beyond producing images of the brain and instead estimate properties of the underlying tissue, such as cell sizes, densities and fibre organisation. These properties can be estimated using biophysical models, but their reliability depends strongly on the MRI measurements acquired, the model being fitted, and the methods used to estimate its parameters. This project aims to develop a user-friendly computational toolbox for exploring the reliability of biophysical MRI models. Building on models already implemented within our lab, the toolbox would allow users to specify an MRI acquisition protocol and model, and then investigate how accurately and robustly different tissue parameters can be estimated. There are several possible avenues for extending the project. These include developing more robust parameter-estimation methods, such as simulation-based inference (SBI); comparing alternative fitting approaches; and developing tools for MRI protocol optimisation—identifying which measurements should be acquired to provide the most reliable estimates of tissue properties. The project will provide experience in quantitative MRI, biophysical modelling, parameter estimation, simulation, optimisation and scientific software development. |
Dr Andriy Kozlov
Profile: https://profiles.imperial.ac.uk/a.kozlov
Contact details: a.kozlov@imperial.ac.uk
| Project title | Description |
| Receptive-field features and nonlinearities of auditory neurons. | The project's aim is to characterise receptive fields and nonlinearities in auditory neurones using new receptive-field analysis methods. Data will be obtained in the lab. Knowledge of data analysis and proficiency in deep neural networks are required. |
| Biomimetic neural networks | This is a machine learning project that is a continuation of our published work: https://www.biorxiv.org/content/10.1101/2023.10.26.564127v1 Proficiency with pytorch is required. It is appropriate for students with a computational background experienced with training ANNs and interested in fundamental questions about natural and artificial neural networks. For more information candidates are encouraged to read the above paper and contact the supervisor. |
| Developing new anaesthetic compounds | This project focuses on a new class of anaesthetic drugs which we synthesise (with Dr Sylvain Ladame) and test using patch clamp. The student will need to be interested in doing chemical synthesis & characterisation, cell culture, and electrophysiology (neuropharmacology). |
Professor Anil Bharath
Profile: https://profiles.imperial.ac.uk/a.bharath
Contact details: a.bharath@imperial.ac.uk
| Project title | Description |
| Learning from Mostly Unlabelled Data | Although data-driven machine learning is now being used to build components of AI systems for medical diagnostics, there is the need to find ways of being able to learn representations that are well suited to new forms of imaging data for which ground truth does not yet exist. This problem is not fully addressed in the mainstream AI field, where it is often assumed that data are readily available with ground truth, and that the (data, label) tuples exist or can be made in sufficient quantities. But, as new ways of imaging or measurement become available, the "data" part of that tuple can be subtly or radically different. Subtle differences can probably be handled by transfer learning, but data that is radically different requires new approaches to learning good deep representations. This project will investigate new approaches to self or unsupervised learning that do not rely on ground truth labels and can be applied to different tasks. Promising approaches are i) contrasting learning and ii) masked autoencoders. This project will focus on investigating both of these in the context of "world models", and increasingly important topic in AI. |
| Uncovering the neural code of artificial neural networks | Deep neural networks can contain hundreds of thousands to millions of parameters collected in a layered organisation. The sheer number of parameters assigned to a single network makes the interpretation of how the network achieves a desired function (such as solving a control task, or a visual task) difficult to understand. Interestingly, experimental neuroscience has developed a series of tools that can be applied to problems of this complexity; even though complete "explainability" of how a neural network works is very difficult, there are nice principles that can be investigated. One of these principles is population codes, by which we mean the manner in which a group of artificial neurons jointly encode a stimulus or state of the world. The same principle can be applied to understanding how an RL agent controls (though a policy) a robot arm, or performs a tracking and detection task, or achieves invariance to transformations during visual tasks. Our aim in this is to use response weighted noise averaging, derived from neuroscience, but applicable to explaining some aspects of neural coding. The project builds on strong work from a previous undergraduate project, and several PhD projects, in order to take a step forward in explainability of neural networks through concepts borrowed from neuroscience. |
| New Approaches to Machine Learning | Many forms of training rely on the existence of labelled data, or simulation environments where reward structures can be easily defined. An alternative approach exists in the area of "Active Inference", where the learning algorithm takes the form of seeking to a) reduce uncertainty about predictions of sensory inputs, b) predict the results of actions on sensory inputs and c) some intrinsic reward that is gained in proposing sensory or motor transformations (such as a change of viewpoint) that improve the performance of a) and b). Currently, the key challenge to exploring these ideas lies within implementation: how should we represent beliefs about the environment in which the agent operates? The goal of this project is to explore active inference using moderate sized _language models_ that have access to variables and sensory input of some form, and harnessing these LLMs either through fine tuning or in-context learning (e.g. an agent rewriting its own text-based instructions for future iterations) to explore the principles of active inference, and to suggest mechanisms to implement effective learners in different domains requiring active sensing and control. Given the increased use of self-modifying agentic systems, this project is rather timely, and may evolve in different directions: from no-code, text based instructions to shift an LLMs behaviour, through to Python-based coding of algorithms for active inference. We know that agentic systems such as "OpenClaw" and "Hermes Agent" are generating lots of hype: can we view these hybrid systems as instances of active inference in action? If so, how does one apply these ideas to more restricted problems, such as vision or control (in the sense of automation), where behaviour is ideally formally and rigorously characterised? |
| Exploring Population Coding Effects in Artificial Neural Networks | Current approaches to exploring both attention mechanisms and activation in deep neural networks often consider $\arg \max_j(a_j)$ where $j$ refers to one of $n$ neurons in a deep network, and $a$ represents its activation level. There are two problems with this approach: a) it biases analysis to the inputs that induce maximum activation, and b) it does not consider how neuronal responses jointly encode the state of the external world, actions or internal beliefs of the network. Whilst divisive normalisation is one of the better computational strategies that explain joint neural coding in biological networks, it remains relatively unexplored, with a few exceptions (see https://arxiv.org/pdf/2411.00393, which has some early work on this, but is clearly outside of mainstream DNN literature). |
| Privacy-Preserving Cross-Task EMG Data Generation for Enhanced Model Generalization Context | ## Background Electromyography (EMG) data is used for advanced human-computer interaction, prosthetics control, and clinical diagnostics. However, collecting high-quality, diverse EMG data is expensive and timeconsuming. Data varies significantly across subjects, sensor placements, and specific motor tasks (e.g., grasping vs. walking). Furthermore, raw EMG signals contain sensitive biometric information capable of identifying individuals, raising severe privacy concerns that limit data sharing and collaborative research. Currently, deep learning models for EMG are often trained on task-specific, isolated datasets. This siloed approach fails to leverage the shared underlying motor control representations present across different physical tasks. This project addresses the dual challenge of data scarcity and privacy by generating synthetic, anonymized EMG data that transfers knowledge between different tasks. ## Project Objective The aim of this project will be to work toward the development of a novel generative AI framework capable of synthesizing privacy-preserving EMG data by learning shared representations across multiple, disparate motor tasks. Generative models (such as conditional GANs or Diffusion models) will be leveraged to utilize source data from one task (e.g., typing, arm moving) to guide and enrich the generation of data for a target task (e.g., complex whole-arm reaching). To address privacy concerns, mechanisms like identitydisentanglement will be integrated during the generation process, ensuring the synthesized signals cannot be traced back to original subjects. The utility of the generated data can then be validated by augmenting training sets and demonstrating significant improvements in the accuracy and robustness of downstream classification or regression models on resource-scarce tasks. By enabling cross-task knowledge transfer while safeguarding user identity, this project could provide a scalable solution for building robust, generalized biomechanical models. ## What You Will Learn This project provides valuable research-oriented skills, including: - Advanced Generative AI: Designing and training state-of-the-art generative models tailored for complex, high-dimensional time-series physiological data. - Privacy-Enhancing Technologies: Gaining a deeper understanding of practical implementations of privacy paradigms and techniques for biometric disentanglement. - Biosignal Processing & Transfer Learning: Developing the skills to extract invariant motor control features, handle noisy continuous signals, and evaluate physiological realism. Programming skills in Python/PyTorch and experience with time-series data will be helpful. ## Supervisory Team [IGS = Imperial Global Singapore] Dr Ling Cheng (IGS) Dr Viktor Schlegel (IGS/Bioengineering) Prof Anil A Bharath Others as necessary e.g. Prof Dario Farina ## Suggested Reading 1. Deep Learning for Electromyographic Hand Gesture Recognition: A Review. Why read this: Provides an accessible overview of how EMG is currently used, highlighting the difficulties of cross-subject and cross-task variability. 2. The Core Problem (Transferability): Cross-Subject and Cross-Task Transfer Learning for EMG-Based Interface. Why read this: Explains the current limitations of siloed EMG models and why finding shared representations across different tasks is highly valuable. 3. The Deep Dive (Time-Series Generation): Time-series Generative Adversarial Networks (TimeGAN). Why read this: A highly technical paper explaining how to generate sequential data (like EMG) that preserves the temporal dynamics of the original signals. 4. The Validation (Utility vs. Privacy): Evaluating the Privacy and Utility of Synthetic Time-Series Data. Why read this: Guides students on how to mathematically and practically evaluate if their generated data is actually useful for downstream tasks while remaining private. |
Dr Amanda Foust
Profile: https://profiles.imperial.ac.uk/a.foust
Contact details: a.foust@imperial.ac.uk
| Project title | Description |
| Engineering Sensory Space | This project will build and calibrate a visual stimulation system for neuroscience experiments, combining screen geometry, eye position and stimulus design to precisely map visual stimuli into the mouse’s field of view. You will develop a system to present spatially controlled visual inputs during brain imaging and ask how neural circuits represent the outside world. The project combines bioengineering, optics, geometry and programming, with scope to develop automated calibration and stimulus-generation tools. Once established, the system can be used to relate precisely defined sensory inputs to neuronal activity, and could be extended to multisensory experiments incorporating auditory stimulation. |
| The Eye as a Window to the Brain: Tracking Arousal and Behaviour During Neural Imaging | How does an animal’s state shape the neural activity we record? Neural activity varies with factors such as arousal, eye position and movement. Pupil diameter provides a useful readout of arousal, while eye movements and other behaviours can help interpret changes in neural activity during experiments. In this project, you will develop a camera-based system to track pupil size, gaze and eye movements during brain imaging, and build an analysis pipeline to synchronise these measurements with neuronal recordings. You will gain experience in imaging, computer vision and quantitative behavioural analysis, with scope to extend the system to monitor whisking, facial movements and locomotion. The goal is to build a robust workflow that helps us distinguish changes in neural activity associated with sensory input, arousal and ongoing behaviour. |
Dr Chiu Fan Lee
Profile: https://profiles.imperial.ac.uk/c.lee
Contact details: c.lee@imperial.ac.uk
| Project title | Description |
| Deep learning and renormalisation group | Deep learning is a type of machine learning algorithms that has been applied to diverse problems in the past decade. A surprising connection between deep learning and renormalization group, a conceptual framework originated from physics in the 70s, has recently been uncovered. This project will study this connection mathematical, and illustrate the relationship concretely using numerical methods. |
| Plasticity in Learning through Spin Glasses |
Dr Chris Richards
Profile: https://profiles.imperial.ac.uk/c.richards1
Contact details: c.richards1@imperial.ac.uk
| Project title | Description |
| Computational actuation: Programming a robotic actuator to behave like a muscle | Skeletal muscle is the universal biological actuator. Despite vast behavioural differences across animal taxa, key aspects of muscle function have remained unchanged through evolution. One such property is that muscle force is not directly controlled by the nervous system. Rather, muscle force is the outcome of nonlinear activation dynamics, force-length and force-velocity properties. These properties confer mechanical behaviour which is profoundly different from conventional robotic motors. Specifically, muscle’s mechanical impedance (stiffness and damping) depend on the muscle’s length, velocity and activation level. Crucially, actuator impedance dictates how a muscle interacts with the mechanical environment. The implications of this are not yet fully clear, though recent theory suggests that muscle’s mechanical versatility may arise from muscle’s “tuneable†impedance. In search of an improved design for robust biologically-inspired actuation, this project will develop hardware which incorporates muscle properties. The project will involve adapting a conventional actuator (e.g. a brushless servo) by building a controller which incorporates activation-length-velocity properties of real muscle. A large part of this project will be developing analogue and digital (e.g. microcontroller) circuitry to “program†the motor to behave like a biological muscle. Once a suitable design has been prototyped, it will be tested under a range of loading conditions (e.g. powering a simplified robotic limb model). |
Dr Chris Rowlands
Profile: https://profiles.imperial.ac.uk/c.rowlands
Contact details: c.rowlands@imperial.ac.uk
| Project title | Description |
| A new way to simulate optical systems | Optical devices are some of the earliest precision instruments ever made, and underpin developments in many fields, not least of which is biology where they have contributed to our understanding of cells, germ theory, neurology and pathology, to name but a few fields. Designing and developing new microscopy tools is therefore of widespread interest, but techniques for simulating optical propagation are either precise but computationally inefficient, or efficient but crude. Small-scale phenomena can be accurately simulated using high-precision finite element methods, which accurately account for diffraction effects, polarization, and nonlinearily, but simulating a microscope objective using these methods is infeasible. Large-scale simulation methods such as ray-tracing can handle macroscopic features, but these simplify the propagation of light considerably, meaning that the user must exercise good judgement when interpreting the results of the simulation. In many optical systems, the propagation through bulk media, such as glass or air, dominate the computational time of the simulation, and as such dramatic time savings can be made by identifying these large, homogeneous regions and not simulating their contents. The student on this project will work on approaches to segment an arbitrary space into a computationally-tractable volume, as well as approaches to combine the finite element simulation with the free-space propagation simulations. As such, a good programming background is very desirable, but enthusiasm and a problem-solving attitude are also important. |
| Developing algorithms to sculpt light in 3D | Photolithography (literally 'light stone-writing') is widely used in the semiconductor industry for patterning microchips, but using light to trigger a chemical or physical change has many uses in biology as well. 3D bioprinting, photodynamic therapy, image recording, and optogenetic control of neurons all employ light to induce a change in a biological system. One important limitation of conventional projection-based optics is that the change is induced by a single photon. This has a subtle problem in 3D applications, because if one wishes to confine the photo-response to a particular plane, the regions above and below the plane are also illuminated. This is a particular problem in optogenetics, a cutting-edge technique in which light is used to excite neurons in the brain. Here, it is desirable to excite one neuron but not the ones above or below, yet this is impossible with conventional optical projection. Fortunately, there is a potential solution - one can use high-speed projectors to make holograms that change very rapidly. None of the projected images are intense enough to trigger a neuron on their own, but the sum of many of them is. One can therefore find a series of patterns that trigger the desired cell, but cause the light above and below the desired cell to 'miss' the important regions, thus having no effect. The student for this project will work on developing an algorithm that can, for a given distribution of neurons, find a sequence of holograms that trigger a single cell but don't affect the surrounding cells. They will develop software to control a projector in order to make these patterns, and if everything goes according to plan, it may be possible to test the algorithms in a laboratory setting. The student for this project should have a moderate to strong mathematical background, and some experience in Matlab or another similar programming environment. If necessary they should have, or be able to develop the lab skills necessary to test their software in real life. |
| Analyzing hyperspectral oncological images using cutting-edge data processing | Deep learning techniques have found considerable use in pattern recognition for image analysis, but in medical imaging there are often additional data dimensions which can be exploited for improved diagnosis. This project will involve one such dataset - hyperspectral Raman images taken from tumour resection margins. In this case, the goal is to identify whether any tumor remains in the image, and if so, where it is located. Neural networks and other deep learning techniques will be used to perform this analysis, incorporating spatial and spectral information to make an accurate diagnosis. |
| Biophotonics | The Rowlands lab develops optical systems (microscopes, spectrometers, displays and so on) for use in biology. Anyone who is interested in designing new systems, building instrumentation, simulation or performing image analysis is welcome. |
| Towards a Raman-Activated Cell Sorting system for cancer screening | Nobody needs to be told how much of a threat cancer poses to the population; even worse, certain types of cancer (such as pancreatic cancer, or certain types of ovarian cancer) are so difficult to detect that once they are observable, the prognosis is very poor. A screening method that can detect the limited number of cancer cells circulating in the blood would be of interest in these cases. Fluorescence Activated Cell Sorting, or FACS, is a routinely-used method for sorting cells into different categories based on fluorescence. Unfortunately, cancer cells aren't fluorescent, and finding a good label is arduous and often ineffective. The alternative is to use some form of intrinsic contrast, such as the Raman effect. The Raman effect allows molecules to be identified by the characteristic vibrational frequencies of the bonds in the molecule itself, thus it is very specific and requires no labelling or staining. The goal of this project is to take the first steps towards a combined Raman-Activated Cell Sorting (RACS) and single-cell sequencing instrument that can identify rare circulating tumour cells early. The student on this project will first be responsible for designing, building and programming a Raman microspectrometer, and then using it to analyse different cell populations (some made up of known cancer cells, some not) to see whether the system can distinguish an individual cancer cell from the thousands of other cells also found in the blood. The ideal student will have a background in programming, some CAD skills, and experience building instrumentation, but these are by no means a requirement; the student will be taught anything necessary that they do not already know. |
| Drugs on Demand - towards an automated synthesis platform | Modern drug synthesis occurs in large chemical plants, or at the very least on a lab bench, and requires extremely well-trained researchers, lots of glassware or plant components, and great expense. This project tries to do away with all of those limitations, allowing essentially any synthesis to be performed on a reconfigurable microfluidic chip. Microfluidics has great promise, particularly for small-scale syntheses, in that it can perform reactions more rapidly, under more tightly controlled and uniform conditions, and in an entirely automated manner. Unfortunately, chip designs for one reaction cannot be easily modified or used for another reaction, which limits flexibility. This new microfluidic chip will be able to emulate any other design, changing reaction conditions and configuration rapidly and easily, ushering in a new era of microfluidic drug synthesis. The student on this project will be working with a postdoc to develop the new microfluidic chip. It uses tiny an array of tiny wax motor valves, so first the student will be responsible for designing and characterizing these valves, before scaling up to larger arrays. The ideal student will have some experience in CAD modelling, design of simple electrical circuits, and basic programming, but these are by no means essential - all candidates will be considered, and any required skills can be taught. |
| Detecting bioweapons with stand-off Raman spectroscopy | Bacillus anthracis, commonly known as anthrax, is a potent bioweapon. Having first been used in World War Two, there have been a number of attacks and close calls, ranging from a 1979 accidental release of spores in the former Soviet Union which killed 69 people, several attempts at terrorist attacks by the Aum Shinrikyo cult in Japan in the 1990s, and the 2001 anthrax letter attacks on senators in the United States. Anthrax is a powerful bioweapon not only due to its pathogenicity, but because it can form spores which are extremely difficult to eradicate. These spores are stable for decades, and are resistant to radiation, ultraviolet light, dessication, extreme heat and cold, as well as a number of chemical disinfectants. Identification and detection of these spores is critical to decontamination of an area after a suspected attack, but for obvious reasons, it is not a good idea for a user to get too close to a suspected contamination. Finding a way to detect these spores at ranges of 10m and above would be extremely beneficial for first-responders who wouldn't have to risk their lives to test a suspected release site. One way to perform this detection is using Raman microscopy. The student on this project will be responsible for building a system to perform Raman detection at long distances, without compromising on sensitivity. This system will be able to detect Bacillus subtilis (a benign analog of anthrax) without the need for the user to come near the sample location, and the project will involve some optical engineering, programming, and potentially some electrical engineering. |
| Speedy Spectroscopy - investigating new ways to speed up vibrational spectroscopy | Raman spectroscopy is an analytical technique which provides a wealth of information about a sample, allowing identification of molecules and even diagnosis of diseases (especially cancer). It requires no labelling of the sample, is extremely specific, and applicable to almost any compound imaginable. Given these virtues, it is fair to ask why it is not more ubiquitous in medical diagnosis, and the answer is that it is painfully slow. Spontaneous Raman microscopy takes around a second to collect even a low-quality spectrum, and this is simply too slow as a tool for mapping tissue, or screening cells. Finding a way to speed the process up would be ideal. In this project you will be exploring techniques to speed up Raman microscopy, for example by using parallel excitation, light-sheet imaging, electron-multiplying CCDs, high-power lasers or high-performance signal-processing methods. Some useful skills might include programming hardware devices / signal processing algorithms, optical alignment or precision machining, but these are not required, and the requisite skills can be taught. |
| World's Fastest Video Camera | High-speed imaging requires specialized cameras to capture fleeting events like explosions, hypersonic flow, or even the passage of light. In this project, we are interested in the oscillations of an ultrasound bubble, which occurs at frequencies of a few megahertz. As such, we will need to build a camera that can image at around one hundred million frames per second, for a duration of around one second. These requirements are far beyond even the fastest cameras available today, necessitating a new development program. The student on this project will be building part of the camera, specifically a small piece of the sensor. Using newly-available silicon photomultiplier arrays, we will be constructing a small-scale prototype with the sensitivity and speed necessary to capture data at these incredible speeds. The ideal candidate will have a good background in electrical engineering, and will be designing and testing readout circuitry for the camera. Once this is complete, they will begin testing a small-scale prototype by building the large-scale optical system required to magnify the bubbles enough to be seen by the sensor. This project will also involve a certain amount of programming, in order to reconstruct the data after the experiment is complete. |
| A New Head Mounted Display Concept: Virtual Reality in a Pair of Sunglasses | In order to experience immersive virtual reality, a display must have a large field of view and a high resolution, otherwise the user will feel like they are 'looking at the world through a toilet roll'. Commercially available head-mounted displays like the Occulus Rift, HTC Vive and Playstation VR solve this problem by placing the screen in front of the eyes, but this is clearly an inelegant solution as it involves basically strapping a brick to your face. More recent designs such as the Microsoft Hololens and Magic Leap One use holographic gratings to project light into the eye, but these have a smaller field of view, leading to the 'toilet roll' problem described above. The Rowlands lab is currently developing a new type of holographic display, which can achieve a large field of view along with high resolution, by making the hologram itself active, rather than passive. Instead of projecting the whole image at once, the display scans a beam across the eye at high speeds, producing the illusion of high resolution but without the compromises needed for the Hololens or Magic Leap One. The student on this project will conduct theoretical and experimental studies into the feasibility of this design. They will be using finite-difference time-domain modelling and fabricating electro-optically active waveguides in an attempt to demonstrate a proof of principle, with the goal of producing a device that can project simple patterns into a stationary eye. The ideal student will have a good background in computer modelling, an interest in microfabrication and photolithography, and possibly some electrical engineering expertise. Any necessary skills can be taught however. |
| Building a next-generation scanning microscope | Scanning optical microscopy is a workhorse tool for modern biology - it can see things deeper into tissue, with 3D resolution, and observe fast dynamic events. Recently, Drs Rowlands and Pantazis have been interested in developing a technology called Primed Conversion (https://www.nature.com/articles/nmeth.3405) in order to make it easier to use for researchers around the world. Primed conversion involves optically tagging cells as they develop, allowing us to trace the development of an organism from a single cell all the way up to a complete animal and seeing which cells are destined to form which parts. The missing piece for the widespread use of Primed Conversion is the integration of the system into microscope systems. The student on this project will build an add-on to a microscope which can perform Primed Conversion, aligning two lasers and scanning them in parallel through the sample. The skills required involve programming, electronic engineering, some mechanical design and some optical engineering, but any skills that the student doesn't possess can be taught. The most important thing is an aptitude for learning quickly and hard work. |
| AstroTIRF: Pinning light to a surface | Total Internal Reflection Fluorescence Microscopy is an imaging technique that can take pictures of cells with incredible resolution - it is able to see things that are the thickness of a virus. While this is very important for imaging of complex cell processes, the limitation is that we can only see the surface of the cell - we can't see inside, as we can with a normal microscope. Nevertheless, it might be possible to interfere two illumination patterns together and combine the high resolution of TIRF with the ability to see features hidden inside the cell. The student on this project will be responsible for delivering on this vision. The student will start this project by modelling the system using optical wave propagation software, before moving on to optics experiments in the lab. Initially work will be on a test system, but eventually will be incorporated into a microscope and used to image cells. The ideal student for this project would have a good background in programming, and some experience with building precise mechanical devices, but the student could be taught anything they need to know. |
| Next-Generation Drug Synthesis: Optimizing bioreactors with lasers | A great many modern drugs are manufactured, not in chemical reactors, but in bioreactors: steel or glass vessels housing many litres of cell culture medium and a colony of genetically-modified cells which produce the drug itself. As this mass-manufacturing technology underpins the production of pharmaceuticals worldwide, there is considerable interest in achieving even modest gains in efficiency and yield which, when scaled out over a large-scale manufacturing process, contribute to dramatic cost-savings. Unfortunately, if optimising a chemical reactor is hard (with all the inhomogeneities in temperature, pressure, reagent concentration and so on), optimising a bioreactor is much harder still, because cells are much more sensitive to their local environment. Fortunately, researchers in the Polizzi lab in Chem Eng, and the Rowlands Lab in Bioeng are working on a way to monitor these cells in situ, using optical imaging and fluorescent reporter cells. The student will work on a system to image the fluorescence from a variety of locations within a large (liter-scale) volume using a large number of optical fibers coupled to a microscope. The student will use the system to monitor reactions in the reactor, and try to reconstruct the resulting fluorescence distribution. The student will need some basic precision manufacturing skills and an ability to prototype ideas quickly, but the most important is a willingness and ability to learn quickly. |
| Advanced Microscopy for Everyone | One of the workhorse instruments in a microscopy suite is the confocal microscope. Unlike a normal microscope, it can image objects in three dimensions, which helps explain why modern laboratories use theirs so extensively, in fields as diverse as histopathology, neuroscience and cell biology. Nevertheless, confocal microscopes are very expensive, costing hundreds of thousands of pounds in many cases, despite containing no particularly expensive parts. This enormous price puts the instrument out of reach of researchers in the developing world, and even several laboratories in developed countries as well. This project will seek to redress this balance, by developing a confocal microscope using modern low-cost rapid prototyping facilities, off-the-shelf microcontrollers and careful design, broadening access to this core technology throughout the world. The student on this project will be responsible for building this instrument, based on a modern design known as a 'rescanned confocal'. This will require some work with a CAD package (like Solidworks), some 3D printing or CNC machining (possibly outsourced) and a bit of programming experience. Students should not be put off taking this project if they don't feel they possess these skills though, as they can be taught. Motivation and a willingness to learn is much more important. |
| Seeing the world in hundreds of colours: SERS tags for biology | Fluorescence microscopy is performed by countless labs around the world, labelling their molecules, proteins, membranes or organelles with a bright, fluorescent label which can be seen under a microscope. Unfortunately, there are a limited number of fluorophores that can be seen in the same image - separating them by their colour gets progressively harder because they all emit over a broad range of wavelengths which are difficult to separate. The same is not true for Raman spectra; these contain very sharp spectral features and can be easily identified from their spectral patterns, but the Raman effect is very weak - taking Raman maps of a surface is very laborious. One solution to this problem is to use the Surface Enhanced Raman Scattering (SERS) effect. SERS occurs when an analyte interacts with a gold nanoparticle, which enhances the electric field substantially. Since the Raman effect scales as the fourth power of the electric field, a modest 100x field enhancement results in 10^8 increase in the Raman effect, making it a bright and efficient molecular tag. This project will be to investigate the use of SERS particles as tags, from modelling the electromagnetic properties of these SERS particles, to using them to image tens of different features in a small cell. This project is quite open-ended, and would therefore suit a range of students, from those interested in computational modelling to people interested in microscopy, instrument development, or even wet chemistry. |
| Watching Sound - creating a new technique for stand-off ultrasound imaging | Ultrasound is one of the safest, cheapest and most powerful ways to image deep within the body. Compared to MRI it is fast, easy to use and significantly less onerous on the patient. Nevertheless, there are limitations which we are working to overcome. All current forms of ultrasound imaging require the user to place an ultrasound probe in contact with the skin. This in turn requires a skilled ultrasound technician to apply ultrasound gel and move the probe to image the organ of interest. A more elegant solution would be to use optical imaging to see the acoustic signal (as well as exciting it), thus removing the need for the technician, gel or even for the patient to lie on a bed. The acoustic signal could be simply recorded by imaging the patient's body with a very fast camera. The Rowlands lab is working on developing optical ultrasound detectors based on evanescent wave sensors; these are extremely sensitive to minute changes in the position of an array of nanoparticles, and thus to a passing acoustic wave. The student working on this project will help develop this new type of ultrasound detector, building the nanoparticle suspension, excitation optics and imaging / readout. The ideal student would have a background in the physical sciences or engineering, with a willingness to try new things and learn. The Rowlands lab is highly multidisciplinary, with lots of different researchers studying lots of different things, so new perspectives and approaches are encouraged. The student can be taught most (if not all) of the skills and techniques they will need to know. |
| Dynamic Dichroic Mirrors - making reprogrammable optical filters for stand-off chemical imaging | Hyperspectral imaging is used in applications from chemical weapon detection to cancer diagnosis, from fraud monitoring to industrial quality control. Currently wide-field camera-based hyperspectral imaging systems are based around single filters - you must know exactly what you're looking for in order to select the right filter. The Rowlands Lab is working on a new type of optical filter which can be reprogrammed at will, allowing arbitrary chemicals to be searched for, for example. Currently, the optical components have been assembled, but need to be tested and new materials tried out. The student on this project will be responsible for taking this system from prototype stage to working tool, and will have to develop a number of skills, from instrument development and debugging, to materials development and optimization and finally development of robust testing methods. There is also the potential for publication or intellectual property development, should groundbreaking advances be made. The ideal student on this project would have a willingness to learn, adaptability and some background in the physical sciences, engineering or computer science. That said, talented students from any background will be considered, and the relevant knowledge taught. |
| Making a true 3D camera | When it comes to microscopes, there are no shortage of approaches to imaging a 3D sample: multiphoton microscopy, light-sheet microscopy, confocal and so on. What is notable about these techniques however is that they work by imaging a volume one plane at a time, and thus aren't really imaging in 'true' 3D. This project will change all that, as the student will be working on a system that can really image a volume (animal heart, brain, cancer organoid, tissue sample etc.) in 3D. The system itself is based on a design called a Framing Camera. This uses a mirror to reflect light to a number of cameras, each of which can see a different plane in the sample. The student in this project will be constructing the prototype of this system, which will involve assembling the cameras and the optical system, programming the mirrors, and ultimately building the world's first true 3D microscope. The ideal student for this project will have a good background in mechanical, electronic or software engineering, and a keen interest in picking up new skills. He or she will be ambitious and self-motivated, and a quick learner. There is no specific requirement on skills as these can all be taught. |
| Intelligent Imaging - tagging and tracking cells in 3D | In collaboration with the Pantazis lab, we have created a new type of microscope that can selectively switch a cell expressing a fluorescent protein from green to red. This is useful for a number of cell-tracking tasks, particularly lineage tracing where the cell of interest needs to be tracked along with all its daughter cells. Now we want to take the next step - programming the microscope to track these cells in real time, and "top up" the colour change where necessary. The student on this project will program the microscope to rapidly scan the sample, mapping out its structure and reconstructing the images into a 3D volumetric dataset. The program will then identify regions in which the photoconversion is lacking, return to those locations and photoconverting them specifically. The ideal student would have a decent programming background, an interest in hardware construction / automation and a willingness to learn new skills. That said, the most important thing for this project is self-motivation - the rest can be taught where necessary. |
| Investigating mice with brain cancer by Raman mapping | Raman microscopy is a powerful technique for mapping the distribution of different molecules. Subtle molecular changes can be recorded and used to assess the disease state of a tissue. The Rowlands lab pioneers high-throughput Raman imaging technologies, and in this project the student will be using some of them to investigate the metabolic activity of a mouse brain tumour provided by the Syed lab (Brain Sciences). The student on this project will be responsible for collecting samples, taking both point-by-point Raman measurements as well as light-sheet Raman measurements, and comparing the two. If needed, the student will make modifications to the Raman instrument in order to improve performance. The student on this project should be willing to learn, able to obtain biological samples competently and reliably, possess good attention to detail, and ideally have some programming and / or mechanical engineering expertise. |
| Virtually Microscopic - building a virtual-reality interface to complex microscopic data | The design of a microscope has remained the same for 350 years: the user looks down an eyepiece, moves a stage and focuses the lens to see features of interest in a sample. Nevertheless, the recent availability of low-cost virtual reality systems means that users need no longer be tethered to the instrument; researchers, doctors and students alike can explore the rich datasets that are gathered by modern microscopy, or even guide the microscope in real time, gaining a new perspective which hopefully leads to new insight. As a researcher on this project, you will have good programming skills and some familiarity with complex Software Development Kits (SDKs). You will be programming a head-mounted display to project part of a large microscopic dataset, updating the display as the user moves around the environment. As the project progresses, you will be incorporating control over the microscope as well, rapidly capturing data to allow the user to explore a sample with as much freedom as possible. |
| 3D Print ALL the Things? | Optics laboratories investigate a diverse range of phenomena, such as brain activity, tumour growth, embryonic development and so on, however the fundamental components used in optical setups are similar across labs and experiments. This compatibility allows components to be commercially sourced and easily integrated in a range of set-ups, however these parts are generally expensive and involve international supply chains which can create a barrier-to-entry for many researchers around the world. In this project the student will design a family of optical components that they will manufacture via 3D printing and test in the lab. The end goal is to create an online database of CAD models and assembly instructions which can be freely shared with researchers around the world, creating cheap and fast access to research equipment. The student on this project will be responsible for designing and manufacturing a range of opto-mechanic components. This will involve work in a CAD package to design components, conversion into a printable format, followed by printing and testing the components. Some experience with either CAD or 3D printing would be advantageous. |
| Making the ultimate colour camera - volumetric holograms for hyperspectral imaging | Most cameras view the world with three colours, but the world is really a symphony of wavelengths from the ultraviolet to the infrared. Importantly these wavelengths help us gain knowledge about the world around us - identifying hazards, mapping disease, informing science and so on. The student on this project will work on the world's first true hyperspectral camera - a camera that can see, not in three colours, but a whole spectrum. This will be created by building a volume hologram which splits each pixel into a large number of sub-pixels, each of which is sensitive to a different wavelength. To start, the student will simulate this volume hologram, making sure that it splits light into an array of different colours with high efficiency and minimal crosstalk. Once the ideal volumetric pattern is determined, the student will go on to manufacture the device, and ultimately test it in the laboratory. The ideal candidate for this project will have a background in basic programming as well as a strong problem solving mindset, but as with all student projects in the Rowlands lab, The necessary skills can be taught. |
| Freezing sound - pioneering high resolution ultrasound through optical tomography | Ultrasound is one of the most widely used medical imaging techniques, yet unlike the camera you're carrying in your pocket, the detector has only a few hundred pixels, severely limiting resolution and field of view. Fortunately, optical cameras can record an ultrasound field with megapixel resolution, substantially boosting medical imaging performance. The student on this project will take the first steps towards tomographic imaging of an ultrasound wave, by observing the sound wave as it passes through a person, changing the refractive index as it compresses and stretches the material it is passing through. This project will start by simulating the ultrasound wave and trying to estimate how sensitive the camera needs to be. The student will then set up an experiment in the lab to see whether measured performance matches simulation. The first reconstructions will be done in 2D but ultimately the system wants to be built in 3D to fully capture any ultrasound wave. Students will ideally have a good background in programming, and have good lab skills; the optics and acoustics knowledge will be taught on the job. |
| SIMaging the future | Optical microscopy is widely used in biomedicine as it is easy to use, safe for almost all samples, fast, and versatile. Unfortunately it has a physical limitation called the diffraction limit which prevents it from observing features smaller than ~100nm or so. Structured Illumination Microscopy (SIM) is a technique for increasing the resolution of an optical microscope. It works by taking nine images of a sample with different interference patterns applied to them, then reconstructing these images, using the Moire effect to work out what the underlying sample distribution looks like. The Rowlands lab has pioneered one of the fastest SIM systems in the world, and is in the process of developing version2 which should be practical an commercializable. The student on this project will be responsible for developing an improved version of the existing instrument that is more robust, compact and easier to use than the original, ultimately so that the instrument can be sold commercially. They should be comfortable handling expensive hardware with care, and ideally have a reasonable programming background (although all necessary skills on this project can be taught). The more important attributes are a willingness to learn and be self-motivated. |
| Turning tissue totally transparent | Within the last year there has been a breakthrough in our ability to control the transparency of live tissues. Typically, light scattering is the main reason why we can't see through things, and scattering is caused by microscale inhomogeneities in refractive index. By exploiting a mathematical relationship called the Kramers Kronig relations, we can now tune the refractive index of an object until it matches that of the rest of the local environment, making it essentially invisible. What the vast majority of people have failed to notice is that rather than just flooding a tissue with another medium to change its bulk refractive index, photopatterning can change the refractive index locally, allowing us to *remove* the refractive index differences, rather than just minimising them. The student will use the multiphoton microscope in the Rowlands lab to selectively photobleach a dye that has been infused in to a tissue, in an attempt to demonstrate that the refractive index can be changed to whatever value the user desires. This will involve wetlab work, potentially some programming and hardware design, and potentially even work with live tissues if the project is that successful. All of these skills can be taught, so the main requirement is a willingness to learn. |
| Edible holography | In the news recently it was announced that a common food dye, tartrazine, could be used to change the refractive index of an object. This is important because being able to structure refractive index on the microscale makes it possible to create true volume holograms, which can be observed in all directions and which are visually indistinguishable from the real object. The student on this project will be using tartrazine infused into Agar gels to create these volume holograms. By photobleaching the tartrazine in a carefully defined pattern, the refractive index could be changed to whatever the user desires, allowing these holograms to be built up one pixel at a time. And because the gels are made out of agar and tartrazine, they are (technically) edible. More seriously, these holograms can act as sensors, probes, vision correctors and so on. On the day-to-day basis the student will be creating gels, testing the photobleaching, designing holograms, and ultimately creating arbitrary three-dimensional holographic objects. This will combine optics, programming, web lab work, and analysis, but as all students in the Rowlands Lab are encouraged to make the project their own, the exact balance of these skills is flexible, and all can be taught. |
| Real laboratory automation | Laboratory automation usually refers to the use of robots to perform experiments and other repetitive tasks without user interaction, but another meaning could be to literally automate a laboratory; to control the lighting, shutters, fans and other noise sources automatically in response to voice commands, interlock conditions, number of users, lab temperature or any other condition. Until recently this will be an extreme expensive endeavour, but the proliferation of the low cost home automation equipment and open source projects such as home assistant mean that this can be explored at comparatively low cost. The goal of this project is therefore to make work in the laboratory safer, easier, more repeatable, and with better logging of conditions than previously has been possible. This will involve significant amounts of programming, a modest amount of hardware design, and possibly some interior decorations skills. Fortunately all of these can be taught, although the interior decoration may be somewhat subjective. More seriously, many accidents happen because users are not paying attention to changing conditions or different environments in the lab. Computers do not suffer from this lack of awareness, and by logging everything that happens in the lab and making sure that conditions are as safe as possible, experimental outcomes can only benefit. |
| FLYdom of movement | The behaviour of many organisms is strongly affected by their movement, which often gets ignored when designing experiments; animals are often fixed in place, suspended on treadmills, balls, or floating platforms, so they can be imaged while "moving", using big, bulky microscopes. This is especially true for flies, which lack the strength or size to carry even a simple imaging system. Nevertheless, these configurations are rarely satisfying as an experimental paradigm; it would be better to move the microscope. This is where we step in. The Rowlands lab is working on a high-performance, lightweight robotic fly-tracking microscope that can move to keep a fly within the field of view as it walks around. The student will be refining this instrument, improving the tracking of flies and ensuring that detailed images may still be taken regardless of how fast the fly moves. Ultimately, the instrument will be used to record neural activity in awake, behaving flies as they interact with their environment. The student who takes this project will have a strong grasp of programming, electronics and an ability to succeed despite challenging the limits of what is possible. |
| Streaming Continuous Optical Nanosecond Events (SCONE) | When high-intensity focussed ultrasound is directed at the brain, in can cause microbubbles injected into the blood stream to break through the blood-brain barrier, allowing drugs and other treatments to reach an organ that is usually carefully protected. Now obviously this has very clear medical utility, but the problem is we have no idea how the microbubble does this, and thus can't optimise the process. SCONE is a project to record an oscillating microbubble at roughly 20 million frames per second, so that when it does break through the blood-brain barrier, we can see what it is doing. SCONE requires computational reconstruction to recover the microbubble data; the student will therefore be employing advanced data recovery and modelling algorithms to address this challenge. A strong background in mathematics and / or programming would therefore be advised. |
| iPhone Imaging Improvements | Colour cameras work by filtering out around 66% of the light hitting them. Researchers in the Rowlands lab have recently developed a method for making those colour filters work with 0% loss, potentially making every camera on the planet three times as sensitive. Currently we need to explore the parameters used to design these lossless colour filters before we start commercializing / selling the design. The student on this project will be working on the design, prototyping and testing of these filters, using advanced simulation tools, nanofabrication and the analytical instruments available in the Rowlands lab. As with anything in the Rowlands lab, no specialized prior knowledge is required and all techniques can be taught. |
| Evading Export Edicts for Illegal Imaging in the Infrared | Infrared cameras can see through fog, paper and bodies, which makes them well suited to clinical microscopy. Unfortunately these cameras are "Export Restricted" - possessing them is illegal if they can image too quickly, or with too much sensitivity, or with high resolution, which makes the ones you can buy pretty much useless for bioimaging... unless you combine them. I want to build an infrared microscope that can see with unprecedented sensitivity, resolution and speed, and that hopefully doesn't get me sent to prison. The student will design and build a testbed to prototype these designs. Any skills necessary can be taught, but the student should possess a can-do attitude, and good problem-solving abilities. |
Dr Dandan Zhang
Profile: https://profiles.imperial.ac.uk/d.zhang17
Contact details: d.zhang17@imperial.ac.uk
| Project title | Description |
| Semi-Autonomous Control for Augmented Robotic Hand Using Tactile Feedback | Introduction and Background: Neurotechnology has advanced rapidly, particularly in the development of neuroprosthetics that aim to restore motor and sensory functions for individuals with limb loss or neuromuscular impairments. However, current robotic prosthetics often lack the ability to provide tactile feedback and semi-autonomous control, which are essential for delicate tasks requiring precision and safe interaction with objects. There is a need for prosthetic systems that can autonomously adjust their grip based on sensory input, enhancing both control and user experience. This MSc project will focus on developing semi-autonomous control algorithms for an augmented robotic hand using tactile feedback. The goal is to create a system that not only allows the user to perform high-level tasks such as gripping and releasing objects but also autonomously adjusts the grip force based on real-time sensor input. This would simulate natural reflexes, such as detecting when an object is slipping or being over-gripped. Objectives: 1. Tactile Sensor Integration: Design and integrate tactile sensors into a augmented robotic hand or prosthetic hand to capture sensory data such as pressure, texture, and contact forces. These sensors will provide critical input for detecting grip states like object slipping or over-gripping. 2. Algorithm Development: Develop algorithms to classify sensor data and detect grip states (e.g., slipping, over-gripping) in real time; Implement control logic that adjusts the grip force autonomously based on sensor input, mimicking natural reflexes. This will enable the robotic hand to react to changes in object handling conditions without user intervention. 3. Simulation and Testing: Simulate the system's performance using a robotic hand model and test its functionality in a series of object manipulation tasks, such as gripping objects of different sizes, shapes, and textures. 4. Performance Evaluation: Evaluate the system’s efficiency in improving grip stability, object handling, and user experience. The focus will be on ensuring that the semi-autonomous control algorithms enhance the user’s ability to handle objects with precision and safety. This project lies at the intersection of neurotechnology, robotics, and machine learning. The development of semi-autonomous control systems for robotic prosthetics or augmented robotic hand can significantly enhance user experience by improving grip stability and reducing cognitive load. With a focus on tactile feedback, this project will contribute to the creation of more natural, reflexive prosthetic hands or augmented hand that adapt to real-world conditions, helping to restore functionality for individuals with disabilities. |
| Event-Adaptive Parallel Decoding for World Action Models | This project will develop a World Action Model (WAM) that dynamically adjusts the temporal granularity of parallel decoding according to anticipated interaction events. During predictable free-space motion, the model will decode longer video–action blocks in parallel. Near contact, grasping, release, or uncertain transitions, it will switch to shorter blocks and more frequent refinement. Unlike existing methods that mainly determine how many actions from an already generated chunk should be executed, this project will adapt the decoding block structure itself, using the WAM’s predicted future visual dynamics and contact-related events. The student will first reproduce an existing WAM, such as LingBot-VA or DreamZero, and then implement and evaluate the proposed event-adaptive decoder. Experiments will primarily be conducted in simulation using selected contact-rich tasks from RoboTwin 2.0. Subject to project progress, the method will also be validated on a real robotic platform. Background: World Action Models jointly predict future visual observations and robot actions, enabling action generation to benefit from learned physical dynamics. DreamZero and LingBot-VA demonstrate the potential of this approach for closed-loop robot control. However, fixed-length action blocks create a trade-off between efficiency and reactivity. Long blocks reduce inference frequency but are vulnerable to prediction errors near contact events, while short blocks enable precise feedback control at greater computational cost. Parallel decoding methods such as PD-VLA improve action-generation efficiency but do not explicitly adapt the decoding structure to the predicted physical phase of the task. Recent methods including PACE, DEHP and Mixture of Horizons dynamically select execution horizons or combine predictions across different horizons. This project instead investigates whether the future visual states predicted by a WAM can be used to change the block size and refinement schedule during generation. Key Tasks: • Baseline reproduction: Reproduce LingBot-VA or DreamZero on a selected subset of RoboTwin 2.0 manipulation tasks. • Event prediction: Identify interaction events from predicted video–action trajectories, such as contact onset, object motion, gripper-state changes, abrupt motion transitions or model uncertainty. • Adaptive parallel decoding: Develop a decoder that generates long blocks during predictable motion and short blocks near important or uncertain events. • Evaluation: Compare against fixed short-block, fixed long-block, conventional parallel-decoding and adaptive execution-horizon baselines. Metrics will include task success, inference latency, model evaluations, action smoothness and contact-related failures. • Real-robot extension: Subject to progress, deploy and evaluate the proposed method on a physical robot. Key Contact: Ms. Lan Wei (l.wei24@imperial.ac.uk) |
Dr Guang Yang
Profile: https://profiles.imperial.ac.uk/g.yang
Contact details: g.yang@imperial.ac.uk
| Project title | Description |
| Synthesizing Immunofluorescence Images from Bright Field Images | Immunofluorescence imaging is valuable for visualizing cellular structures but is costly and resource-intensive. Bright field imaging is more accessible, so creating immunofluorescence images from bright field images can make detailed cellular analysis more widely available. The student will need to use a generative adversarial network (GAN) trained on paired bright field and immunofluorescence images to generate synthetic fluorescence images from bright field inputs. The target is to develop a tool that accurately produces immunofluorescence images from bright field images, providing detailed cellular insights without the need for expensive staining techniques. |
| Concept and Visual guided Large Vision Language Model for Medical Report Generation | Different imaging protocols can produce varying image data even for the same type of cells. To address this, the student will develop a generative adversarial-based or diffusion-based model trained on paired images obtained from different cell painting protocols. The goal is to standardize these images, making data from diverse protocols consistent and comparable. This tool will harmonize cell painting images, enhancing the reliability and accuracy of cellular analysis without the need for uniform imaging conditions. |
| Enhancing Cell Image Segmentation through Data Harmonization | Cell image segmentation is crucial for accurate cellular analysis, but variations in imaging protocols can affect segmentation quality. The student will develop a harmonization algorithm that standardizes cell images from different protocols before segmentation. By training a machine learning model on harmonized datasets, the tool will improve segmentation accuracy across various imaging conditions, making it easier to analyze and compare cellular structures. |
| Self-supervised learning for cell imaging foundation model | Cell imaging generates vast amounts of data, but labeled datasets are often limited and costly to produce. Self-supervised learning (SSL) offers a way to leverage large amounts of unlabeled data for pretraining models, which can then be fine-tuned with limited labeled data for specific tasks. This approach can significantly improve the performance of models in various cell imaging applications, such as segmentation, classification, and feature extraction. |
Dr Huai-Ti Lin
Profile: https://profiles.imperial.ac.uk/h.lin
Contact details: h.lin@imperial.ac.uk
| Project title | Description |
| Bioinspired microglider design with micro-robotics fabrication techniques | Biomimetic micro‑gliders are important for understanding insect gliding flight and developing distributed environmental sensing networks. This project focuses on designing and prototyping a tandem‑wing micro‑glider inspired by gliding insects. We will build on prior high‑performance designs from Dr. Lin’s lab and incorporate micro‑robotics fabrication techniques from Dr. Jayaram’s lab to engineer wing‑hinge articulations for in‑flight control. Key elements: Design and prototype tandem‑wing gliders; iterate using lightweight materials and origami‑inspired mechanisms. Integrate articulated hinges for controllability; evaluate stability, glide ratio, and maneuverability. Test in Aerial Robotics Lab (e.g., launch rigs, motion capture); optional wind‑tunnel characterization in Dr Lin's Lab as needed. Who should apply: Students interested in origami/mechanism design and hands‑on micro‑fabrication. You must be comfortable assembling millimetre‑scale parts with tweezers under a microscope. If not, this project is unlikely to be a good fit. The project can accommodate up to two students working in different but complementary fronts (pending total lab capacity). Please email Dr Lin (h.lin@imperial.ac.uk) your CV and research interest with subject title: 2026-27 BioMicroGlider MEng project. |
| Control of flexible wings for oscillating wind energy harvesting | This project investigates structural control of aeroelastic flutter on an oscillating flexible aerofoil in a low‑speed wind tunnel, with applications in structural safety (vibration damping) and energy harvesting (power amplification). Dr Lin’s lab has a research stream on wing sensory system which investigates the neural representation of wing deformation. See paper for context https://www.pnas.org/doi/10.1073/pnas.2518032122 To build a physical model, Lin Lab has developed a flexible‑wing evaluation platform with precise pitch control for wind‑tunnel testing. The system currently operates in open‑loop to induce oscillations. In collaboration with Dr Jayaram’s lab, we will integrate micro‑fabricated strain sensors on the wing and collect data across a range of oscillation conditions. Using these data, we will develop and test control strategies (moving toward closed‑loop operation) to modulate vibration for energy harvesting and quantify device performance. Who is suitable Strong mechanical design skills and interest in fluid dynamics/control, flexible wings, and energy harvesting. Basic electronics and microcontroller experience (e.g., Arduino for sensor‑feedback control). Comfortable assembling millimetre‑scale components under a microscope. This is important. Expected outcomes Instrumented flexible‑wing test article with strain sensing. Datasets across operating regimes, with analysis of flutter onset and response. Prototype control strategies (e.g., feedback damping, energy‑harvesting amplification) and a performance report with recommendations for next steps. Please email Dr Lin (h.lin@imperial.ac.uk) your CV and research interest with subject title: 2026-27 OsciFlexWing MEng project. |
| Wirelessly powered, ultralight, and low-power neural telemetry device | Dr Lin's lab has an unique neural telemetry backpack designed for insect weighing under 50mg. The system leverages RF energy harvesting and back-scatter communication to operate the device in a 1~2m range. With a new collaborator at University of Washington, we aim to develop the next generation wireless neural telemetry system with much longer range using an unique Bluetooth backscatter technology. While the system is designed for insects and we will use insects for evaluation, it can be used for other systems. The micro-SD card size make it highly flexible for wearable devices and highly compatible for implantable applications. This is a project suitable for students with background and interest in micro-electronics, wireless communication, RF engineering, circuit design, BCI interface, neural signal processing. The project can accommodate up to two students working in different but complementary fronts. Please email Dr Lin (h.lin@imperial.ac.uk) if you are interested with subject title: 2026-27 NeuroTelemetry MEng project. |
| Neuromorphic Edge Computing | Neuromorphic chips allow hardware implementation of spiking neural networks (SNNs) which is energetically efficient. In this project, we will explore applications with modern neuromorphic chips in the single-board-computer form factor. The range of topics includes: 1. Physiological signal compression: perform signal feature extraction using SNN to reduce the data bandwidth for transmission/logging. 2. Real-time image processing: object detection with with minimum computation. 3. Simple motor control: train a controller to control a motor and compare to a standard PID controller. This project is suitable for student interested in minimum neural network operating under the energy constraints in edge computing applications. Study outcomes are highly relevant to wearable devices, mobile robots, smart cameras...etc. Email Dr Lin (h.lin@imperial.ac.uk) if interested with the subject title: 2026-25 Neuromorphic MEng Project. |
Dr Jo Jackson
Profile: https://profiles.imperial.ac.uk/johanna.jackson
Contact details: johanna.jackson@imperial.ac.uk
| Project title | Description |
| Structural and functional integrity of synapses in human Alzheimer's Disease | Synapse loss is a key feature of many neurodegenerative diseases, including Alzheimer’s Disease (AD), and understanding synapse loss and synapse dysfunction alongside other pathology and ‘omic changes would enable a greater understanding of the time course of synapse loss. Studying synaptic changes in human post mortem tissue increases the translational validity of drugs taregting synapses. Here we aim to characterise the structure and function of synapses in the human condition in order to develop drugs to specifically target synpases and alleviate cognitive impairment. The imaging of cleared human tissue will be optimised before synapse structural and subtype markers and their relationship to amyloid and tau pathology being assessed in cleared human tissue with confocal or two-photon imaging. We will also test the feability of a functional synaptic readout and if it can be used in human post mortem tissue. All changes will be verified by staining and imaging of syanptoneurosomes or synapse markers in human tissue sections. Analysis of the synapse proteome will provide new knowledge on the synaptic pathology in AD to enable us to develop therapies to directly target the synapse in AD to ultimately lead to improvement in the cognitive impairment associated with the disease. |
Dr Jun Ishihara
Profile: https://profiles.imperial.ac.uk/j.ishihara
Contact details: j.ishihara@imperial.ac.uk
| Project title | Description |
| Effect of cancer immunotherapy on mental health | "Recent research revealed that excess immune system activation causes mental illness. Cancer immunotherapy is a revolutionary invention to fight cancer through activating the immune system. Together, it is suggested that immunotherapy may cause mental illness, however the molecular mechanism between immunotherapy and mental illness remain unclear. Serotonin and histidine are crucial molecules for regulating mental condition. Parry Hashemi lab has developed a biosensor that can monitor the serotonin/histamine level in the brain in vivo. Jun Ishihara is an immuno-engineer, who can produce immunotherapy drugs in his laboratory. Jun can make immunotherapeutics both unmodified versions (which rapidly distribute to the whole body after injection; thus systemic therapy) and engineered versions (which stick to ECM proteins at the injection site; thus localised therapy). Our central hypothesis is that cancer immunotherapy causes mental illness through serotonin/histidine concentration changes and cytokine release, and this could be solved by a drug delivery system approach. Systemic cancer immunotherapy may cause mental health issues. Engineered cancer immunotherapy that can localise at the cancer would reduce the mental health issue. This research will shed a light on connection between immunotherapy and mental illness, and can propose a solution for immunotherapy-induced mental illness. Students can learn various aspects of immunotherapy and neurotechnology related techniques." |
Dr Kaushik Jayaram
Contact details: k.jayaram@imperial.ac.uk
| Project title | Description |
| Distributed Tactile Sensing | The project involves creating cockroach inspired antenna with distributed 1D and 2D mechanosensors (inspired by insect campaniforms). We will understand the role of active antenna movements for enhancing sensing and tactile discrimination. These antennae will be integrated on insect-scale robots and demonstrate high-speed tactile SLAM-based navigation (in the dark). Will involve collaborations with Dyson School of Engineering. Expect strong interest in microfabrication, nano-3D printing, laser processing. Experience with clean room procedures and microcontroller programming is an advantage. |
| Modeling of Insect-scale Shape Morphing Robots | The project involves modeling the kinematics and dynamics of insect-scale bioinspired shape morphing robots in IssacGym/ Mujoco to create digital twins. These models will be used for training machine learning algorithms and for developing AI bioinspired controllers for navigating complex terrains. Will involve collaborations with Computer Science Department at Imperial and at ETH Zurich. Prior experience with physics-based modeling softwares in a must. |
| Digital twins of spiders | The project involves modeling the kinematics and dynamics of arthropods in Unity/ Mujoco to create digital twins using high-fidelity tracking data collected (DeeplabCut, Replicant) from spiders moving on a treadmill at varying inclinations (vertical, lateral and upside down). These models will inform the creation of new bioinspired gaits to be deployed on insect scale robots. Involve collaboration with other insect labs in department. Expect a strong background in data processing, programming, AI/ML. Prior experience with computer graphics and modeling is an advantage. |
| Firefly inspired Optical communication for Swarming Drones | The project involves creating a nanoquadrotor (less than 60mm) capable of emulating firefly like communication (flashing and response). The project will involve mechanical design, electronic fabrication of custom controller boards and software development for developing firefly inspired strategies. We expect to field test this system by the end of the project to demonstrate active closed loop communication with fireflies as the first step towards understanding complex signalling. Will involving collaboration with international teams. Looking for background in prior drone design, control and programming. Experience with building custom electronics is an advantage. |
| Plant-sensing insect robots | The project involves creating custom sensors, manipulators and attachment mechanisms for insect-scale robots to sample and deliver biomolecules to plant tissues. Interest in plant science/ environmental monitoring is an advantage. Strong background in flexible electronics design and integration along with software development is preferred. Involves collaboration with other departmental and international teams. |
| Backend and frontend interface for neural recording insect backpack | The project involves creating software, both backend (embedded programming using Zephyr) and frontend (python/java/etc) to log data, perform communication and remote telemetry/control for custom insect backpacks being developed in the group. Participants are expected to be independent and have prior experience with software development using one or more of the tools/packages mentioned above. |
| Backend and frontend interface for microrobot control | The project involves creating software, both backend (embedded programming using Zephyr) and frontend (python/java/etc) to log data, perform communication and remote telemetry/control for custom electronic boards being developed in the group. Participants are expected to be independent and have prior experience with software development using one or more of the tools/packages mentioned above. |
| Software for remote and structural health monitoring using NDE sensors onboard insect robots | The project involves creating software, both backend (embedded programming using Zephyr) and frontend (python/java/etc) to log data, perform communication and remote telemetry/control for custom electronic boards that interface with NDE sensors (eddy current and ultrasonic sensors) being developed in the group. Participants are expected to be independent and have prior experience with software development using one or more of the tools/packages mentioned above. Familiarity with NDE sensors is an advantage. |
| Dung-beetle inspired burrowing robot | The project involves creating 3-4cm autonomous robot inspired by dung-beetles that can dig, burrow and clear narrow tunnels (similar body size as a robot). Strong background in design, small scale manufacturing, flexible electronics design and integration along with software development is preferred. Involves collaboration with other departmental and international teams. |
| Insect-scale Micro-Flying Drone | Create a swarm of smallest flying quad rotor drones (35mm rotor to rotor). This includes hardware design, software development and potential electronics prototyping to create a group to about 5 drones flying in different formations. Broad skillset spread across mechanical hardware, electrical hardware and software are required. |
| Voice coil actuators for microrobots | The project involves creating novel ultralight weight, mm-scale electromagnetic actuators for insect scale robots. Strong background in mechanics, electromagnetism, electronics design and integration along with software development is preferred. Involves collaboration with other departmental and international teams. |
| Piezoelectric soft actuators for microrobots | The project involves creating novel piezoelectric (PVDF) actuators for insect scale robots. The project involves coming up with novel approaches for stacking ultra thin layers of flexible materials. Familiarity with screen printing and electrode deposition are ideal. Strong background in mechanics, modeling and integration is preferred. Involves collaboration with other departmental and international teams. |
Dr Laki Pantazis
Profile: https://profiles.imperial.ac.uk/p.pantazis
Contact details: p.pantazis@imperial.ac.uk
| Project title | Description |
| Beyond fluorescence: watching genetically-encoded bioharmonophores assemble in live mammalian cells | Fluorescent proteins have shaped modern cell biology, but they come with trade-offs: they bleach, saturate, and blur under high-intensity light. These limitations cap what we can see—especially when tracking fast or subtle dynamics over long periods. What if we could express a label that never fades, never saturates, and produces a clean, quantifiable signal—one that’s visible through even the densest cellular environments? This project gives you the opportunity to build and validate a new class of genetically encoded imaging probes—bioharmonophores—in live mammalian cells. These are not fluorescent proteins. Instead, they are small peptides that self-assemble into non-centrosymmetric nanocrystals inside protein shells, generating second-harmonic generation (SHG) signal: a narrow, photostable, and unbleachable optical readout. At the heart of the system is a synthetic expression circuit: peptides with strong SHG potential are targeted to encapsulin nanocompartments, where they remain inert until released by a genetically encoded TEV protease. This two-part design allows for controlled liberation and local concentration of SHG-active sequences, triggering their self-assembly into crystalline structures that produce strong SHG signal when imaged. This project focuses on the critical proof-of-principle: can we trigger SHG-active peptide self-assembly inside living human or mouse cells? This has never been shown. Success would confirm that bioharmonophores can be expressed, activated, and imaged in standard cell culture, opening the door to genetically programmable, multiplexed SHG imaging in deep tissues. Computational/Practical student experience You will: • quantify SHG signal strength across time and conditions using an existing Zeiss two-photon microscope platform • analyse optical signatures to correlate peptide identity, structure, and signal intensity. This project gives you hands-on experience with synthetic gene circuits, nonlinear optics, mammalian cell engineering, signal quantification, and peptide design—a complete pipeline from molecule to imaging outcome. Outcomes By the end of the project, you will produce the first demonstration that genetically encoded bioharmonophores can self-assemble and generate SHG signal in live mammalian cells. |
| Programming the first cancer cell: engineering spatially confined CRISPR to trigger and track oncogenic mutations in living organoids | Cancer begins when genetic change arises in individual cells embedded within otherwise normal tissue. Yet conventional genome-editing approaches perturb populations simultaneously, making it difficult to observe how one newly mutant cell interacts with its neighbours and reshapes a tissue. Our laboratory is developing an optically controlled CRISPR platform to overcome this limitation. Cas9 is sequestered through the photocleavable protein PhoCl and released by Primed Conversion, in which intersecting laser beams provide highly localised optical activation. Recent work in our lab established proof-of-principle light-dependent Cas9 activity and demonstrated spatially confined Primed Conversion within individual cells in 3D organoids. This project will engineer a next-generation light-gated CRISPR circuit with improved OFF-state repression and high light-induced dynamic range. The student will quantitatively characterise alternative circuit designs using molecular cloning, flow cytometry and live-cell microscopy, then transfer the optimised system into a 3D organoid model to test single-cell-restricted genome editing. Depending on progress and student interests, the project will extend to perturbing a disease-relevant endogenous gene, such as APC, and longitudinally tracking the resulting clone within surrounding wild-type tissue. The project combines synthetic gene-circuit engineering, CRISPR, optogenetics, organoid biology, advanced microscopy and quantitative image analysis. Principal Supervisor Name Periklis (Laki) Pantazis Principal Supervisor Email p.pantazis@imperial.ac.uk Secondary Supervisor Name Professor Mark Isalan Secondary Supervisor Email m.isalan@imperial.ac.uk |
| Towards single-cell precision ultrasound neuromodulation using optically traceable Bioharmonophores | Focused ultrasound provides a powerful non-invasive approach to neuromodulation, but its spatial resolution remains constrained by the acoustic focal volume, which typically encompasses many cells. This project will investigate a different strategy: using ultrasound as a broad, subthreshold energy source while defining the site of neuronal activation through spatially localised piezoelectric nanotransducers. We will test biodegradable peptide-based bioharmonophores as optically traceable nanotransducers. Bioharmonophores are polymer-encapsulated, self-assembling aromatic peptide nanoparticles that generate strong second-harmonic-generation (SHG) signals and biodegrade in biological environments.¹–³ Because ordered diphenylalanine assemblies exhibit pronounced piezoelectric properties,â´â€“ⶠwe hypothesise that suitably engineered bioharmonophores can convert ultrasound-induced mechanical stress into a highly localised electrical stimulus. The central hypothesis is that bioharmonophore-associated neurons can be activated at ultrasound exposures below those required for direct ultrasound neuromodulation, while neighbouring particle-negative neurons exposed to the same acoustic field remain inactive. If achieved, stimulation precision would be determined primarily by nanotransducer localisation rather than by the ultrasound focal volume. |
| Synchronised to Silent: Watching Piezo1 Signalling Break Down Under Shear in Real Time | Cells in epithelial tissues do more than just survive mechanical stress—they interpret it, synchronise their responses, and adapt. One of the most powerful molecular sensors orchestrating this process is Piezo1, a mechanically activated ion channel that converts shear stress into calcium signals. When activated, Piezo1 channels trigger transient calcium influxes, which can be monitored using live-cell biosensors. But how exactly do individual cells encode mechanical input? How do they coordinate their responses across space and time—and what happens when that coordination fades? This computationally based project gives you the opportunity to answer these questions using a rich, high-quality dataset of time-lapse fluorescence images acquired from GenEPi biosensor-expressing epithelial cells under shear stress. GenEPi is a genetically encoded calcium reporter that lights up when Piezo1 channels open, enabling direct, real-time readouts of mechanosensitive activity in live tissues. Crucially, the data have been collected under defined flow conditions using a custom-designed microfluidic platform, meaning you will be working with dynamic, structured datasets that closely reflect physiological mechanical environments. What makes this project particularly compelling is the discovery that Piezo1 activation is not only force-dependent, but also transient and coordinated across cells—until it isn’t. With continued or repeated stimulation, the system begins to lose its coherence: signal amplitudes decline, synchronisation breaks down, and the tissue shifts from a unified responder to a fragmented, desynchronised field. What governs this transition? Can it be predicted or quantified? These are not just questions of cell signalling—they touch on the principles of collective behaviour, adaptation, and signal fidelity in living systems. Your role in this project will be to explore, quantify, and model these dynamic transitions using image processing and computational analysis. By extracting time-resolved GenEPi fluorescence from single cells and tracking those responses across multiple stimulation cycles, you’ll uncover how shear stress modulates both the strength and structure of Piezo1 activity. You’ll also compare how different flow conditions—low vs. high shear—and cytoskeletal states affect signal propagation, adaptation, and decay. The work will involve building or adapting pipelines for image segmentation, signal extraction, time-series analysis, and population-level synchrony metrics. Computational/Practical student experience You will: - process and analyse large-scale time-lapse imaging datasets of GenEPi-expressing cells under controlled shear stress, - use Python, MATLAB or ImageJ/Fiji to extract ΔF/F₀ calcium signals from individual cells and clusters, - quantify signal dynamics over time: amplitude, rise time, duration, and frequency, - develop computational tools to measure intercellular synchronisation, using pairwise correlation, signal entropy, or phase-locking indices, - perform comparative analyses across shear conditions and treatment states to assess signal adaptation and coordination loss. This is a fully desk-based, computational project requiring no lab work or cell culture. You will work with already-acquired, publication-quality datasets and develop analytical methods that directly contribute to an active manuscript. The project offers deep training in image analysis, time-series data handling, and biological signal interpretation—skills with wide relevance in both academic and biotech contexts. Outcomes By the end of the project, you will have generated a series of analytical outputs—plots, heatmaps, and coordination metrics—that reveal how Piezo1 responses change over time and under stress. Your work will illuminate how force signals propagate through epithelial tissues, how collective behaviour is initiated and lost, and how cellular coordination is shaped by both mechanical inputs and intracellular architecture. These results will not only strengthen the current understanding of Piezo1 function but also provide a general framework for analysing other dynamic biosensor systems in mechanically active contexts. |
| Advancing Mechanobiology with ChemiGenEPi Biosensors | Cells are constantly pushed, stretched, and squeezed—and they feel it. Mechanobiology is the science of how cells sense and respond to forces, a process that drives development, organ function, and disease. At the centre of this force-sensing machinery is Piezo1, a pressure-sensitive ion channel. Our lab has already introduced GenEPi, the first genetically encoded Piezo1 activity reporter, but we want to go further. What if you could watch Piezo1 at work in real time, in living tissue, with sensors that are brighter, more stable, and tunable across colours? This project gives you the chance to build exactly that: ChemiGenEPi1.0, a brand-new chemigenetic biosensor. By combining the genetic precision of GenEPi with the dye-based flexibility of WHaloCaMP, ChemiGenEPi will let us see Piezo1 activity with unprecedented clarity—from single cells in culture to organs/organoids and whole animal models. Computational&Practical student experience You will: • test its performance in live-cell imaging assays (brightness, photostability, dynamic range), This project is hands-on at the interface of molecular engineering, synthetic biology, and cutting-edge imaging. You’ll gain experience in biosensor design, live-cell fluorescence and FLIM microscopy, and in vivo validation. Outcomes By the end, you’ll have helped create a next-generation tool for watching mechanosensitive signalling in action—one that could reshape how we study force in biology and even inspire future drug discovery. |
Dr Swathi Padmanabhan
Profile: https://profiles.imperial.ac.uk/s.padmanabhan
Contact details: s.padmanabhan@imperial.ac.uk
| Project title | Description |
| Computational Design Of Induced Chiroptical Probes For Disease Biomarker Detection | Many biologically important molecules, including glutamate and lactate, are key markers of metabolism but do not have strong, useful optical signatures. This project will explore a new strategy: designing molecular probes that become optically active when they bind to these target molecules. The key concept is induced chiroptical response, where binding to a chiral biomolecule produces a measurable optical signature in the probe–target complex. You will combine molecular modelling and machine learning to predict these chiroptical responses and guide the design of new probes. The project will first establish a computational framework for predicting induced-CD responses in probe–target complexes. Building on this framework, you will investigate and design new chiral probes for biomolecules such as glutamate and lactate, opening up new possibilities in imaging and drug delivery. If there are any questions, please reach out to Swathi Padmanabhan (s.padmanabhan@imperial.ac.uk). |
Professor Dario Farina
Profile: https://profiles.imperial.ac.uk/d.farina
Contact details: d.farina@imperial.ac.uk
| Project title |
| Wearable neural interface for neurorehabilitation with physiological characterisation |
Dr David Labonte
Profile: https://profiles.imperial.ac.uk/d.labonte
Contact details: d.labonte@imperial.ac.uk
| Project title | Description |
| Body size and the cost of the neural control of movement | Movement is integral to all animals, and emerges from a coordinated interaction between a nervous control architecture and the musculoskeletal system. Traditionally and today still, these two elements are often considered in isolation. But no muscle will generate a force without being instructed to do so, and brains are embodied and thus useless without muscles to actuate – the two systems must be in tune. The ideal tuning may be reasonably expected to change with animal size: an elephant must worry about gravity much more so than an ant, and it is widely accepted that the musculoskeletal system adapted to meet this and other mechanical constraints. What is much less clear is how size impacts the costs and optimal strategy of neural control, and how both interact with size-specific changes to the physical environment. In this project, you will begin the building of a bridge between the two involved fields – neuroscience and biomechanics. You will tackle two related problems. At first, you will estimate the costs of neuronal computation across animal body sizes, and compare it to the costs involved in muscle-driven locomotion. Are neural control costs significant, and how does their relative importance vary with animal size? Next, you will link established mechanical consequences of changes in body size, such as increased limb cycling periods, with equivalent but understudied changes in the optimal neural control strategy, including demands on nerve action potential velocity, muscle activation times and more. Do mechanical and neuronal constraints vary in synchrony, or are demands on one steeper, so that it takes a dominant role in shaping animal movement across body sizes? Suggested reading Hooper, S. L. (2012). Body size and the neural control of movement. Current Biology, 22(9), R318-R322. More, Heather L., and J. Maxwell Donelan. "Scaling of sensorimotor delays in terrestrial mammals." Proceedings of the Royal Society B 285.1885 (2018): 20180613. Attwell, D., & Laughlin, S. B. (2001). An energy budget for signaling in the grey matter of the brain. Journal of Cerebral Blood Flow & Metabolism, 21(10), 1133-1145. Laughlin, S. B. (2001). Energy as a constraint on the coding and processing of sensory information. Current opinion in neurobiology, 11(4), 475-480. |
Dr Hayriye Cagnan
Profile: https://profiles.imperial.ac.uk/h.cagnan
Contact details: h.cagnan@imperial.ac.uk
| Project title | Description |
| Dual-site transcranial alternating current stimulation for tremor control | Involuntary shaking is a common symptom of Parkinson’s Disease and Essential Tremor, affecting around one million people in the UK. This project aims to leverage plasticity—brain’s ability to adapt and change—for therapeutic purposes by delivering well-timed electrical inputs to key regions across the tremor network. Based in the Cagnan lab, the focus will be on piloting dual-site stimulation of the motor cortex and cerebellum to achieve longer-lasting therapeutic benefits for tremor patients. Your role will include (1) modelling the volume of tissue activated during dual-site stimulation, (2) developing and testing closed-loop control algorithms and (3) developing approaches for efficient optimisation of stimulation parameters. We are looking for a student with strong skills in engineering, instrumentation, and programming, along with a background in neuroscience. 1. Schwab BC, König P, Engel AK. Spike-timing-dependent plasticity can account for connectivity aftereffects of dual-site transcranial alternating current stimulation. NeuroImage. 2021;237:118179. doi:10.1016/j.neuroimage.2021.118179 2. Schwab BC, Misselhorn J, Engel AK. Modulation of large-scale cortical coupling by transcranial alternating current stimulation. Brain Stimulation. 2019;12(5):1187-1196. doi:10.1016/j.brs.2019.04.013 3. Saturnino GB, Madsen KH, Siebner HR, Thielscher A. How to target inter-regional phase synchronization with dual-site Transcranial Alternating Current Stimulation. NeuroImage. 2017;163:68-80. doi:10.1016/j.neuroimage.2017.09.024 4. Fleming JE, Sanchis IP, Lemmens O, et al. From dawn till dusk: Time-adaptive bayesian optimization for neurostimulation. PLOS Computational Biology. 2023;19(12):e1011674. doi:10.1371/journal.pcbi.1011674 5. Cagnan H, Pedrosa D, Little S, et al. Stimulating at the right time: phase-specific deep brain stimulation. Brain. 2017;140(1):132-145. doi:10.1093/brain/aww286 |
| Modulatory role of transcranial stimulation on cognitive control | Everyday decision-making depends on our ability to adapt and sometimes stop actions unexpectedly. This skill can range from something as simple as resisting a tempting slice of cake to something as critical as hitting the brakes in an emergency. Cognitive control can be compromised in a range of neuropsychiatric disorders and remains difficult to restore using invasive and non-invasive brain stimulation techniques. We previously targeted the medial prefrontal cortex, a key brain region involved in response inhibition, using transcranial electrical stimulation to modulate neural rhythms and associated behaviors. This project, based in the Cagnan lab, will focus on (1) data analysis of electrophysiological and behavioral responses, (2) stimulation artifact removal, and (3) modeling behavioral and electrophysiological data. We are looking for a student with strong signal processing skills and a background in neuroscience. Tuning the brakes – Modulatory role of transcranial random noise stimulation on inhibition Mandali, Alekhya Torrecillos, Flavie ... Cagnan, Hayriye et al. Brain Stimulation: Basic, Translational, and Clinical Research in Neuromodulation, Volume 17, Issue 2, 392 - 394 |
| Phase Transitions in Circadian Tremor Patterns | Involuntary shaking is a common symptom of Parkinson’s Disease (PD), affecting approximately 150,000 people in the UK. Tremor in PD can be triggered or influenced by various factors throughout the day (e.g., stress or medication intake), making it crucial to identify trends that are key to effective clinical management. The project will take place in the Cagnan Lab and will involve analysing a unique dataset consisting of long-term (2 years) recordings of PD patients collected via wearable sensors in free-living conditions. Tremor events in this dataset have already been identified through machine learning algorithms. With this project, we will explore the circadian dynamics of tremor in PD over several days using recurrence quantification analysis (RQA), a nonlinear data-driven technique that provides objective markers for regularity, trends, and phase transitions in time series data. A particular focus will be on the impact of patients’ medication schedule changes on these dynamics. We are seeking a motivated student with programming and signal processing skills who is eager to deepen their understanding of the circadian progression in neurodegenerative disorders. |
| Sleep Fragmentation in Parkinson’s Disease and its impact on tremor | Sleep fragmentation, characterised by frequent awakenings or disruptions, has a significant impact on daytime functioning, leading to increased fatigue, reduced motor control, cognitive decline, and heightened stress and anxiety. In Parkinson’s disease (PD), sleep fragmentation can diminish a person’s ability to manage and compensate for daily tremors, worsening their symptoms' severity and duration. While recent evidence supports this connection, a systematic and comprehensive study with a representative PD cohort and long-term follow-up is still lacking. This project aims to investigate sleep fragmentation from multiple angles and assess how it affects the severity and duration of daily tremors in PD patients. The research will be conducted in the Cagnan Lab, utilising a unique dataset containing two years of long-term recordings from PD patients in free-living conditions, with tremor events already identified by machine learning algorithms. We are looking for a motivated student with strong programming and signal processing skills who is eager to better understand the relationship between sleep fragmentation and Parkinson’s disease symptoms. |
| An Investigation into the Role of Cerebellar Transcranial Electrical Stimulation on Cognition in Essential Tremor Patients | |
| The Role of Cerebellar Transcranial Electrical Stimulation on Cognition in Essential Tremor Patients | |
| Developing a closed-loop simulation | |
| Identifying cross-frequency coupling of gamma, theta oscillations alongside beta bursts |
Professor Darryl Overby
Profile: https://profiles.imperial.ac.uk/d.overby
Contact details: d.overby@imperial.ac.uk
| Project title | Description |
| Brain-On-Chip: A Novel Explant Perfusion Model for Ex Vivo Studies of Living Brain Tissue | The neuroscience community aims to study neural and glial cells in their native microenvironment. Typically, this requires intravital techniques, such as implantation of intracranial windows, or advanced 3D models such as organoids. For example, the blood-brain barrier is a key factor regulating transport into brain tissue, for example for chemotherapeutics or large molecules used to treat cancer or neurodegenerative disease. In this project, we aim to develop a device to preserve the viability and function of murine brain explants for ex vivo analysis of neural function. The project builds on a successful design already applied to liver explants. Briefly, liver tissue placed in our perfusion device exhibits flow through blood vessels, recreating blood flow as occurs in vivo. We now wish to extend the applications to brain tissue. This project will interface with a team of researchers to adapt the current explant-in-chip design to murine brain. We aim to measure viability markers as a function of perfusion time, assess flow through the microvasculature, and measure neural cell function using voltage-sensitive dyes. |
Dr James Choi
Profile: https://profiles.imperial.ac.uk/j.choi
Contact details: j.choi@imperial.ac.uk
| Project title | Description |
| Visualising Sound using Machine Learning and/or Signal Processing Algorithms | Purpose. The purpose of this project is to develop a deep neural network or beam forming algorithms that can reconstruct the location of acoustic sources using multiple microphones. Motivation. In therapeutic ultrasound, a focused ultrasound transducer is used to concentrate energy to a point in the body, allowing us to noninvasively and locally manipulate tissue (tumour ablation, drug release from acoustically-active particles, etc). Our laboratory developed therapeutic ultrasound devices for delivering drugs to the brain (across the blood-brain barrier) for the treatment of brain cancers, neurodegenerative diseases, and other neurological conditions. However, the success or failure of the technique has been difficult to track as clinicians are unable to directly observe what is happening within the body. An emerging way of monitoring this procedure is with the use of microphones located around the focused ultrasound transducer. Sound generated during the procedure are captured by the microphones. We then reconstruct an image of the treated area using passive beamforming algorithms. The reconstruction of a signal source based on multiple sensor signals is broadly known as beamforming. In addition to medical imaging, it is used in underwater acoustics, astronomy, and other disciplines. The problem with many existing passive beamforming algorithms is the poor spatial resolution in the reconstruction of the sound sources. This means we can't precisely locate where the source is coming from. The purpose of this project is to develop a deep neural network and/or signal processing methods that can reconstruct an image of the treated region with better accuracy and spatial resolution. Work description. This work will involve generating training data using computer simulations on a Matlab toolbox known as k-wave. We will then train the deep neural network on PyTorch or develop fundamental signal processing algorithms. We will explore conventional neural networks such as convolutional neural networks, recurrent neural networks, and others; and, potentially, more advanced techniques, such as transformers and physics-inspired neural networks. |
| Fast Tracking of Physical Objects in Virtual Reality (VR) and Augmented Reality (AR) | Purpose. Create a physical object in the real world that can be tracked in the virtual world with speed and accuracy Motivation. Virtual reality (VR) and augmented reality (AR) provide an infinite world that users can interact with. In many applications, we would like to project our real-world body (hands, legs, etc.) and objects (a ball, stick, cup, etc.) into the virtual space. However, current methods still remain slow, clunky, and inaccurate, breaking the immersive experience that we hope for and creating a disconcerting feeling when using VR and AR. What are the current methods for tracking objects and what are the physical, hardware, and computational limitations? How can we improve upon these limitations? Work. You are tasked with building a physical object that includes trackable sensors: infrared light-emitting diodes (LEDs) and inertial measurement units (IMUs). The IMU provides very fast positional tracking (1,000's of Hz), but suffers from poor accuracy over time. The infrared LEDs provide highly accurate tracking, but is slow (10's of Hz). We will therefore provide fast tracking with IMU that is corrected by the infrared LEDs over time. This combined IMU + infrared LED tracking provides a rapid and accurate method of tracking objects. The object's position will be recreated in a virtual world using Unity. The speed and accuracy will be quantified. You will then explore ways of improving upon key limitations with this method, such as tracking out of the field of view, or adding capabilities, such as user feedback with haptic vibration motors. |
| Ultra-High-Speed Video Camera at 10 Million Frames per Second | Purpose: Develop an ultra-high-speed video camera at 50 million frames per second. Motivation: Certain phenomena, such as ultrasound imaging and therapy with microbubbles (contrast agents) operate in the MHz rate. Such fast dynamics cannot be captured using traditional cameras, which operate at around 60 Hz. Commercially available cameras can reach 1 million frames per second, which is still not enough. And while a 10 million frames per second camera is available on the market for £200k, that camera cannot capture more than 256 frames (25.6µs of data). We propose a new video camera concept that could reach up to 50 million frames per second, capable of capturing nearly unlimited amounts of frames. By creating this device, we would be able to observe phenomena in biological tissue that no one has been able to observe. Outside of the domain of biomedical engineering, this camera could be used to image plasma in fusion reactors, high-speed objects in space, and other high-speed phenomena that requires incredibly high frame rates. Work: This project requires electrical engineering skills. The students would be asked to make circuits that are connected to a unique sensor array. If the analog circuit is successful, we would then require some digital electronics skillsets and optics (physics). |
| Visualising Sound - A Neural Network for Passive Acoustic Beamforming | Visualising Sound - A Neural Network for Passive Acoustic Beamforming |
| An Ultra-High-Speed Depth Camera | We propose an ultra-high-speed depth camera. This camera would be able to not only capture optical images, but also see how far away those objects are. What makes this camera unique is that it would be ultra-fast. It would be able to track depth of very fast moving objects; or track depth when the camera is on a very fast moving object (eg, a drone). The camera would be very versatile, being able to track people, survey lands, and more. There is also the potential for this camera to be adapted for medical imaging. We are still in the very early stages of sensor development. The first task would be to work with a single optical sensor and then progressively work to a 2 by 2 array, 4 by 4 array and so forth. Required skillset: Electronics Engineering. You'll need a basic understanding of how to build analogue circuits. |
| Microbubble generation and characterisation for drug delivery using therapeutic ultrasound | Motivation: Focused ultrasound and microbubbles can be used to alter the permeability of the blood-brain barrier. However, variability in the microbubble design can lead to variability in the procedure itself. This includes unpredictable safety and performance profiles. Purpose: The purpose of this project is to characterise microbubbles using acoustic and/or optical methods. Work: Students with different skillsets can work on this project. This includes chemists, physicists, computer scientists, and engineers. |
| Hydrogel-based microchannels for studying acoustic cavitation | Create hydrogel-based capillary channels down to 5-10 µm in diameter. See https://onlinelibrary.wiley.com/doi/full/10.1002/smll.202301163 |
| Optimising of an ex vivo brain slice platform for studying acoustic cavitation-mediated blood-brain barrier opening | Develop methods and devices for studying mechanical phenomena in ex vivo brain slices. This may include many goals depending on the student's skillsets. For example, improve the health of the tissue and ensure that it is as close to physiological relevance as possible. Other examples include improving methods for interrogating the brain slices, such as injection of molecules directly into vessels, and increasing the throughput of these experiments. |
| Serum biomarkers of blood-brain barrier disruption induced by focused ultrasound and microbubbles. | Analyse blood samples for evidence of a change to blood-brain barrier permeability. |
| Scalable Digitisation and Readout Architecture for Large SiPM Arrays | This is electrical engineering work to create a camera using an array of SiPM sensors that can count individual photons. You'll be building the hardware the converts analog electrical signals into digital outputs. This platform can be used for high frame rate imaging (millions per second), depth imaging, and other applications. |
Professor Martyn Boutelle
Profile: https://profiles.imperial.ac.uk/m.boutelle
Contact details: m.boutelle@imperial.ac.uk
| Project title | Description |
| Novel microfluidic devices for organ on/in a chip monitoring. | The MGB group has an expertise in-tissue monitoring using microfluidics sampling devices coupled to electrochemical sensors and Biosensors for measurement the real-time concentrations of key ions, metabolites and neurotransmitters. This lab-based project is part of an effort within the group to adapt our microfabricated sampling technology to engineer a sampling component of an organ on/in a chip device. Such devices offer the exciting possibility of complete control of the tissue environment allowing study of organ function mechanisms, and testing of drugs. In the project the student will learn how to make 2D microfluidic devices using laser cutters, paralene film coaters and a range of other microfabrication technologies. They will help design a new device for use within an organ on/in a chip microfluidics device and test performance. Applications of the devices are wide and include organs, muscle and cells. Successful devices will be tested in collaboration with Professor Overby's group. the project will be supported by MGB and POSTDOC Dr Sally Gowers |
Professor Mengxing Tang
Profile: https://profiles.imperial.ac.uk/mengxing.tang
Contact details: mengxing.tang@imperial.ac.uk
| Project title | Description |
| High Frame Rate Cardiac Imaging using ultrasound | Increasing the frame rate of medical ultrasound imaging is of high benefit for functional analysis of cardiac function, where High Frame Rate Ultrasound (HFRUS) should be required to facilitate the examination of the temporal behavior of fast changing cardiac anatomy and blood dynamics. Current clinical echocardiography typically achieve a standard frame rate (40-80Hz), which is still below the clinical needs in rapid cardiac events observed such as stress echocardiography and fetal echocardiography. Plane/diverging wave ultrasound is a typical method for HFRUS but has low Signal to Noise Ratio (SNR) due to motion artefacts. On the other hand, phase array HFRUS implemented simultaneously by Multi-Line Acquisition (MLA) and Multi-Line Transmission (MLT) has a potential of imaging at >400 Hz, while keeping a relatively high SNR. MLA transmits a wide beam and receive several beams simultaneously and usually can facilitate fast spatial scan. MLT transmits short excitation pulses during a short interval and receives/constructs multiple beams at the same time. Combining MLA and MLT could provide up to 4-6 times higher frame rate while the image quality remains not compromised comparing to traditional cardiac imaging. The aim of this research is to verify these methods in simulation and in experiments to demonstrate the feasibility of ultrafast phase array cardiac imaging to achieve up to ~400Hz frame rate without significantly compromising image quality. Proposal 2: Mapping cardiac electrical activity using Ultrasound Electromechanical Wave Imaging Cardiac electrical activation mapping is clinically recommended for diagnosis of patients with heart failure and arrhythmia. It often provides anatomical structures combined with cardiac functional information, and can provide more information than electrocardiogram (ECG) and thus being potentially more accurate. However current cardiac electrical activity is usually obtained with intracardiac electroanatomic mapping, which is invasive, costly, time-consuming with high-risk. Electromechanical Wave Imaging (EWI) is a novel ultrasound-based technique that can noninvasively map myocardial activation of all four chambers at high temporal resolution (500-2000 frames per second). EWI can be implemented with transthoracic ultrasound with the potential of single-heartbeat diagnosis. Based on previous clinical trials, the accuracy of EWI in predicting arrhythmia location is higher than that of standard ECG, thus leading to reduced ablation time, costs and the need of repeat procedure in the future. This proposal aims at obtaining high quality 2D EWI results with transthoracic ultrasound using the open ultrasound platform, and to show the clinical potential of this method in animals or human. |
| Depth-Resolved Blood Flow Velocity Estimation for Transcranial Doppler Ultrasound | Background Accurate estimation of cerebral blood flow velocity is critical for diagnosing cerebrovascular conditions. Transcranial doppler (TCD) uses ultrasound waves to measure cerebral blood flow velocity in the major intracranial arteries, which can be an indicator of increased stroke risk for children with sickle cell disease [1]. TCD provides blood flow spectra based on a single element transducer or color-coded blood flow based on a phase/linear probe. Both requires three-dimensional knowledge of cerebrovascular anatomy and its variations to localise targeted arteries as well as precise probe positioning for tracking/monitoring. Besides, the interpretation of the blood flow spectra for diagnosis and treatment demands professional training and practical experience, making the TCD study highly operator dependent. Recently, volumetric ultrasound imaging has been developed and explored for transcranial blood flow imaging [2], enabling comprehensive visualisation of intracranial arteries with minimised manual adjustments. In this project, a deep learning framework is proposed to directly estimate blood flow velocity from volumetric doppler ultrasound data (in human). Aims and methods: A deep learning model will be trained to estimate blood flow velocity using doppler signals. Different data preprocessing including IQ, phase, and radiofrequency will be explored. The proposed method will be compared against conventional doppler processing (FFT-based and autocorrelation methods). The proposed framework will be validated using simulation, flow experiments, and in vivo human volunteer data. Skills: Knowledge of ultrasound simulation software such as Field II and k-Wave; practical experience of deep learning (Keras, PyTorch) is preferable but not mandatory References: [1] R. Adams et al., “The Use of Transcranial Ultrasonography to Predict Stroke in Sickle Cell Disease,” New England Journal of Medicine, vol. 326, no. 9, pp. 605–610, Feb. 1992, doi: 10.1056/NEJM199202273260905. [2] P. Xing et al., “3D ultrasound localization microscopy of the nonhuman primate brain,” eBioMedicine, vol. 111, Jan. 2025, doi: 10.1016/j.ebiom.2024.105457. |
Professor Holger Krapp
Profile: https://profiles.imperial.ac.uk/h.g.krapp
Contact details: h.g.krapp@imperial.ac.uk
| Project title | Description |
| Fly-robot interface - neuronal control of biohybrid system | Neurobiological research on the integration of signals obtained from different sensor systems is often limited by the fact that the experimental animals are stationary, i.e. they are fixed to a holder within an experimental setup. The reason for this is that stable electrophysiological recordings from nerve cells are only possible if there is no relative movement between the recording electrode and the nerve cell. While this approach has successfully been used to discover some general principles of sensory information processing, it has two fundamental limitations: (i) under natural conditions the animal simultaneously receives input from virtually all of its sensor systems, and (ii) it generated motor commands (efference copies) which may modify the way in which sensor signals are being processed. To approximate more realistic conditions when monitoring neuronal activities in the fly nervous system the animal will be mounted on a robot that is free to move in the laboratory environment. The action potentials of motion sensitive interneurons in the fly visual system will be recorded and used to control the steering of the robot, establishing a closed-loop fly-robot interface. This configuration will allow the experimenter to selectively disable individual sensor systems to assess their respective impact on multisensory integration in the fly nervous system. This project is dedicated the necessary miniaturization of electrophysiological equipment, e.g. amplifier circuits, so it can be mounted on a mobile robot platform to record the neural signals of identified optic flow processing interneurons. The recorded signals will be filtered and converted into command commands for the robot steering. The resulting feed-back system will be set up in a way that prevents the robot from colliding with any obstacles in its immediate environment. |
| Electrophysiological characterization of optic flow processing interneurons in flying insects. | The visual system in many animals and humans contributes to state change estimations by analysing panoramic retinal image shifts known as optic flow. Earlier studies in blowflies have revealed the underlying neuronal mechanism which is believed to complement state change estimation based on mechanosensory/inertial systems. This project aims to support the idea that optic flow processing in the visual system of flying insects is tuned to control species-specific natural modes of motion which are determined by the animal's flight dynamics. Experimental evidence in support for the mode sensing hypothesis requires a comparative study of the receptive field properties of motion sensitive interneurons in the visual systems of other than dipteran flies, e.g. species belonging to the order of orthopteran, odonata and lepidoptera. Hoverflies, which show distinctly different flight patterns than blowflies and also perform differently in behavioural gaze stabilization experiments, would be an ideal candidate species. Preliminary experiments have shown that hoverflies, too, employ motion sensitive interneurons in their visual system. But only one such interneuron has been studied so far. This project requires the dissection of flying insect species for extracellular recordings upon visual motion stimuli. The neuronal responses will be analysed using customized MATLAB/Python programmes which reveal the cell's receptive field organization. From the electrophysiological results the preference of individually identifiable interneurons for specific self-motion components (state changes) will be derived. |
| Insect Wing Beat Analyzer | To understand how the insect nervous system controls specific tasks such as flight and gaze stabilization we generate experimental stimuli, as input - for instant, visual motion across the animals eyes - and measure the time course of a relevant behavioural parameter. One important behavioural output parameter is the difference between the left and the right wing beat amplitude, delta WB. In the fruitfly Drosophila, it has been shown that delta WB is proportional to the torque the animal generates when following the direction of visual motion to minimize retinal image motion. This project aims to build a so-called 'wingbeat analyzer' which enables the measurement of delta WB. It is based on the difference between the output signals of two photodiods that are differentialy illuminated due to different wing beat amplitudes either side of the animal. The wing beat analyzer will enable simultaneous measurements of a behavioural output parameter, delta WB, and the neuronal signals contributing to its control. Ultimately, the project is relevant for the development of novel bio-inspired control architectures applied to autonomous robotics. |
| 3d head rotations in blowflies | This desk-based project aims to retrieve fly 3D head rotations from 2D high speed videography data. By combining Image analysis methods with 3D model fitting techniques, it studies the correlation of head roll, pitch, and yaw rotations induced by visual and inertial stimuli to understand the structural organization and control of the fly neck motor system. The results are relevant for the development of image stabilization systems mounted on small autonomous robotic platforms. |
| Neuronal integration of polarised light and motion vision in flies and butterflies | Many animals, including flies and butterflies, exploit patterns of linearly polarized light to guide tasks as diverse as navigation, water seeking, mate recognition, communication, and host detection. Polarized light cues in natural scenes follow distinct spatiotemporal patterns, and visual systems have consequently evolved to process polarization cues within this dynamic context. Whilst polarization cues for communication and host detection are also thought to be processed as structured spatiotemporal signals, the mechanisms by which polarization information is integrated with object detection and motion vision pathways in these contexts is less well understood. This project aims to measure neuronal responses of motion sensitive interneurons to moving patterns of polarised light in flies and butterflies. The project will involve mounting and dissecting insects for extracellular electrophysiology, collecting wild flies and butterflies in the summer, and data analysis in Matlab. Programming experience is useful but can be learned as part of the project. |
| Electrophysiological characterization of forward model adaptation in walking flies | A stable and accurate perception of the world ultimately demands a distinction between the sensory consequences of self-motion, and those arising from external changes – a failure to do so would result in a futile cycle of responding to our own motion. Recent neurophysiological evidence suggests that flies anticipate the sensory consequences of their actions (a so-called forward model) to prevent stabilization reflexes from occurring in response to volitional movements. In theory, this forward model must adapt to account for variable neuronal delays and changes in the sensory environment. This project will test this hypothesis by measuring the electrophysiological responses of motion sensitive interneurons in the fly brain whilst the fly is walking within a closed-loop virtual reality platform with control over the latency of sensory feedback. This project will involve dissecting flies to access the brain whilst they walk on a spherical treadmill; extracellular electrophysiology of motion sensitive neurons during walking; data analysis in Matlab or Python. Programming experience is useful but can be learned as part of the project. |
| Closed-loop polarotaxis in flying insects | Many insects, including flies and butterflies, orient themselves relative to the pattern of polarised skylight. This is thought to underly a form of navigation or spatial orientation, termed polarotaxis. Previous results in the lab suggest that flies are more likely to orient to a polarised light pattern when they are free to rotate, rather than when tethered in place on a wing beat analyser that enables the measurement of the animal’s intended turns. This difference may arise due to the absence of visual feedback when tethered in place and can be tested by closing the loop such that the animal’s movement is fed back to update the polariser position. The aim of this project is to compare polarotaxis in tethered flying insects, including flies and butterflies, both under open- and closed-loop conditions. Methods will include image analysis for pose estimation (including neural network training in Python using DeepLabCut), programming stepper motors using Arduino, and data analysis in Matlab or Python. Familiarity with programming is helpful but could be learned at the start of the project. |
| Electrophysiological characterization of forward model adaptation in walking flies | A stable and accurate perception of the world ultimately demands a distinction between the sensory consequences of self-motion, and those arising from external changes – a failure to do so would result in a futile cycle of responding to our own motion. Recent neurophysiological evidence suggests that flies anticipate the sensory consequences of their actions (a so-called forward model) to prevent stabilization reflexes from occurring in response to volitional movements. In theory, this forward model must adapt to account for variable neuronal delays and changes in the sensory environment. This project will test this hypothesis by measuring the electrophysiological responses of motion sensitive interneurons in the fly brain whilst the fly is walking within a closed-loop virtual reality platform with control over the latency of sensory feedback. This project will involve dissecting flies to access the brain whilst they walk on a spherical treadmill; extracellular electrophysiology of motion sensitive neurons during walking; data analysis in Matlab or Python. Programming experience is useful but can be learned as part of the project. |
Professor Julien Vermot
Profile: https://profiles.imperial.ac.uk/j.vermot
Contact details: j.vermot@imperial.ac.uk
| Project title | Description |
| Modelling cell shape changes in complex morphogenetic processes | Cardiac morphogenesis is a process that is often perturbed in Congenital Heart Diseases. We use zebrafish as a model organism to study morphogenesis of the cardiovascular system at cellular resolution. This project will focus on the initial steps of cardiac valve formation. The student will study the cellular behaviours associated with the formation of site of development of the cardiac valve called the atrioventricular canal. It is a tube-like structure that delimitates the ventricle and the atrium of the heart. The cellular behaviours, such as cell shape and volume changes, during the developmental process are crucial to the canal formation but are not well understood. In this project, we will cell-based computational model to study quantitatively the impact of cell morphology and dynamics during atrioventricular canal formation. |
Professor Manos Drakakis
Profile: https://profiles.imperial.ac.uk/e.drakakis
Contact details: e.drakakis@imperial.ac.uk
| Project title | Description |
| Reciprocity properties of networks of memristors | MATHEMATICAL (MATLAB) PROJECT - Memristors are novel nano-elements theorised in 1976 but only recently (2008) fabricated by Hewlett Packard Labs. Qualitatively speaking memristors' operation and dynamics resemble naturally encountered synapses. The fabrication though of memristors is still in its infancy. The project investigates the reciprocity properties of networks of memristors and how/whether these can be reliably used in encryption. The student will investigate in detail the reciprocity properties of simple networks of ideal identical memristors and, ideally, the use of such networks for the encryption of biomedical signals. Ideally the student will be using Mathematica code but Matlab is also acceptable. Intended for a good YR4 student; or for MSc students with a strong EEE degree. (Keywords: mathematica/matlab/simulations) |
| Revisiting low-power CMOS Hodgkin-Huxley dynamics realisation | TRANSISTOR LEVEL/MATHEMATICAL PROJECT -Tough non-linear transistor-level design focusing on the celebrated and Nobel-prize winning Hodgkin-Huxley dynamics. Do not choose this project unless you love realising Matlab by means of transistors! This project is based on rich previous work and can include both transistor-level design and matlab. It provides the student with the opportunity to master transistor-level design and to familiarise oneself with the use of industrial level software. The scope of the project is to unlock the exact synthesis of the Hodgkin-Huxley dynamics by revisiting both the transistor-level design and the mathematical approximations of the original non-linear dynamics.The project is intended for a student who has excelled in EE-1, EE-2, Bioinstrumentation and Signals & Systems courses; or for MRes students with a strong EEE degree. (Keywords: mathematics/transistors/cadence) |
| Study and Simulation of a full Cochlear Implant Processor and Stimulator | This is a transistor-level project which allows the student to master Cadence. You will be given certain already designed blocks and you will be asked to put together a full cochlear processor and stimulator. You will understand transistor-level design choices and trade-offs, architectural level constraints and you will be testing/evaluating the architecture that you designed by importing and processing in cadence different audio files. A project relying heavily on simulation. At the end of this project you will be known for your Cadence skills. |
| 4R Biosensor Longevity Strategy: An Electronics-Enabled Strategy for the Acquisition ofLong-Term High-Quality Electrochemical (Bio)Sensor Data | PRACTICAL ELECTRONICS/PROJECT: Electrochemical sensors and biosensors are powerful tools for in-situ monitoring.On the other hand long term operation of sensors in high cell density/high protein environments is beset by problems collectively referred to as biofouling. They originate by the absorption of proteins and cells to the sensor surface and results in the build-up of a layer that reduces mass transport rates, causes changes in the local environment and passivates the sensor surface. In operational use this is experienced as falling sensor sensitivity which ultimately limits longevity.Whilst there are strategies in the literature that mitigate against these effects none offer sufficiently robust or long-lived solutions. This project aims at investigating/realising a solution which can be codified as Recalibrate, Regenerate, Reconfigure and Replace (4R strategy) miniaturised biosensors. The 4R longevity strategy is realisable my means of appropriate tailor-made electronics. |
| An Energy efficient F0 estimator | (A software desk based project (100% MATLAB)) At the cutting edge of Cochlear Implant research is a massive ongoing effort in improving their effectiveness. Several Stimulation Strategies (SS) are employed to convey relevant information to individuals with hearing loss. In traditional SS (such as CIS and CA) users find it difficult to understand and appreciate tonal based languages and speech in noise. There exist promising results showing that speech formants (especially F0) provide considerable comprehension to tonal based languages by including the F0 contours in the SS. In this project you are to compare current existing methods of F0 extraction (e.g. Autocorrelation, cepstrum ..) evaluate their energy efficiency as well as its hardware implementation feasibility, you are also encouraged to propose a novel technique in estimating/extracting F0 from signals we can currently extract (Instentaneous frequency, envelope slow/fast). Project route: 1. Survey existing F0 algorithms 2. Implement the algorithms in MATLAB /SIMULINK 3. Compare their accuracy/complexity/efficiency (Major Milestone) 4. Either: a. shortlist algorithms implementable in hardware (This is a safety net) b. propose a new/hybrid method prioritising efficiency/complexity 5. Extra: if student has enough time left to propose hardware blocks for said implementation Advantages: • From the student’s perspective this project is very achievable in software(MATLAB) • Gives a taste of research-based projects • There are areas where a student can express creativity (proposing a hybrid F0 estimator) • This is a mostly signal processing project (many students take advanced signal processing in other Depts) • There are ‘safety nets’ in the project if the student does not manage to propose anything new as they would be able to achieve a thorough comparison of existing F0 estimators. |
| Investigation and implementation of novel artefact suppression methodologies in bidirectional electrophysiological interfaces. | Deep Brain Stimulation involves both stimulating by means of strong pulses and recording very weak brain signals while stimulating. However, the strong stimulating pulses generate (stimulation) artefacts which contaminate or even destroy the recording of the weak signals. You will be involved in the design of innovative analogue, digital, or mixed-mode approaches for achieving real-time artefact-free biopotential recording, directly from the stimulation site. Experimental testing results using different electrode arrays, along with head-to-head comparison results (with existing artefact elimination techniques) are expected to be delivered. The project is suitable for students who wish to master (micro)electronics by testing their ability to design and test very high-performance circuits. Publication possible. |
| Design and delivery of a miniaturised, wearable, and high channel-count biopotential acquisition device. | You will be involved in the design of multichannel and high-performance prototype devices that have the potential to be used in a noisy clinical environment. Design trade-offs have to be taken into consideration and an optimal solution must be identified for the successful delivery of a noise-robust instrument that can provide low-noise recordings from multiple neuronal targets. You will gain experience of the full design cycle of a device and you will master PCB design. Such skills are snatched by well-paying companies/employers. |
| Design and delivery of a complete stimulation and recording system for DBS and SCS applications. (DBS=Deep Brain Stimulation; SCS=Spinal Cord Stimulation) |
You will be involved in the design of a state-of-the-art instrument that aims at providing both stimulation and recording capabilities from neural targets within the human brain and spinal cord. Various artefact suppression methodologies that require timing indicators from the stimulator, along with their real-time capabilities, will be investigated. A demanding project leading at the mastering of PCB design, stimulation topologies and recording limitations. A project suitable for ambitious candidates aiming at conducting research later or being employed in the field of medical devices. Do not take this project unless you enjoy the thrill and the challenge of producing a useful and robust instrument. |
| Micro-instrument for Traumatic Brain Injury (TBI) Monitoring | Design and realisation of a micro-instrument for the neuroelectrochemical monitoring of the injured human brain. |
| Modular Open platform for novel ultrasound imaging techniques | The development and commercialization of novel ultrasound device require research instruments that allow the user full control of the transmission sequences and access to the recorded echoes at the different stages of the receiver pipeline. You will be involved in the design of a modular ultrasound instrument for low-cost ultrasound imaging. The instrument is being used to develop novel diagnostic tools. This is a demanding project for those who wish to have a career in the medical device field. You will be designing and testing modules for a low-cost imager. The project carries humanitarian value. SKILLS: PCB design, FPGAs. The ambitious student who will thrive in this project will be ready for both research and industry. |
| Stimulation artefact suppression during closed-loop DBS and cardiac stimulation | Investigation of circuit-level techniques aiming at the suppression of stimulation artefacts. |
| Investigation of biosignal acquisition architectures with and without analog front-ends | Typical signal readout topologies incorporate a customised for the targeted biosignal front-end. In this project the student will investigate the possibility of realising simpler data acquisition architectures which do not benefit from the presence such a customised front-end. Such a bold move: a) leads to simpler and lower power overall designs, a fact which supports wearability, but b) comes at the expense of less good quality of the recorded signal; however, the quality of the interfaced signal can be restored by post-processing in the digital domain. Design and performance trade-offs between architectures with and without front-ends will also be investigated. Skills: mastering simulation tools; design and physical layout of pcbs. |
| Design of Capacitorless High-performance EMG and EEG Application Specific Integrated Circuit (ASIC) in 180nm technology | Recently we have shown that it is possible to realise very large time-constants without the use of very large in area standard integrated capacitors. More specifically, it has been shown that by combining MOS-based capacitors (MOSCAPs) and large-value pseudo-resistors, it is possible to create such large time-constants needed for band-limiting the EEG and EMG biosignals. The very high capacitive density (capacitance per unit area) of MOSCAPs leads to very significant capacitor area reductions. We have already built 64/128 channel 24-bit conversion ASICs-based nodes in 0.35um AMS technology. This project is about investigating the migration of the above basic techniques and designs onto newer 180nm technologies; comparative performance trade-offs will also be investigated. Skills: mastering Cadence simulation tools and ASICs design. |
| Experimental evaluation and optimisation of a new Simultaneous Impedance-Electromyography Recorder (SIER) instrument | Recently we have been working on the realisation of a new multi-channel skin stimulation and EMG recording instrument (concurrent stimulation and recording). SIER instruments can offer unique highly sought after real-time impedance and sEMG information which in turn can be used for feeding learning models and/or robotic/prosthetic devices. The project will involve the conduction of electrode-skin contact impedance (ESCI) and sEMG recording experiments by means of an already existing SIER instrument, the optimisation of the SIER instrument and the study of techniques for the post-processing of ESCI and SEMG data. Skills: mastering lab-based/application testing skills and matlab-based post-processing skills. |
| Study of the “High-Pass Pole Shifting†technique for Bidirectional Electrophysiological Interfaces | In general, closed-loop neurostimulation setups underpin the scheme: (1)stimulation > (2)readout of the stimulation response > (3)making sense of the read out response to the stimulation > (4)control/adjustment of the stimulation chacteristics > (1) delivery of adjusted stimulation. Such general schemes have been under investigation for conditions such as Parkinsons, tremor etc. The “High-Pass Pole Shifting†technique focuses on step (2) of the closed-loop scheme described above and involves timely and clever switching of carefully chosen and adjustable analog-front-end blocks which facilitate the readout stage (3) without sacrificing quality of the target biosignal (which, typically, is much weaker than the stimulation artefacts) by minimising the impact of stimulation artefacts during recording. This project is primarily a simulation project which will investigate the incorporation of fractional order transfer functions in the switched blocks of the front-end and in particular their effect in the recovery time of the targeted weak biosignal in the presence of strong stimulation artefacts. Skills: mastering simulation tools, fractional order systems and matlab-based processing and analysis tools. |
| Migration of the “High-Pass Pole Shifting†technique for Bidirectional Electrophysiological Interfaces on silicon | In general, closed-loop neurostimulation setups underpin the scheme: (1)stimulation > (2)readout of the stimulation response > (3)making sense of the read out response to the stimulation > (4)control/adjustment of the stimulation chacteristics > (1) delivery of adjusted stimulation. Such general schemes have been under investigation for conditions such Parkinsons, tremor etc. The “High-Pass Pole Shifting†technique focuses on step (2) of the closed-loop scheme described above and involves timely and clever switching of carefully chosen and adjustable analog-front-end blocks which facilitate the readout stage (3) without sacrificing quality of the target biosignal (which, typically, is much weaker than stimulation artefacts) by minimising the impact of stimulation artefacts during recording. The new technique has been confirmed by pcb-level prototypes. This project aims at investigating the migration and realisation of the “High-Pass Pole Shifting†technique at silicon/ Application Specific Integrated Circuit (ASIC) level. Skills: mastering Cadence simulation tools; depending on time constraints opportunities for layout as well. |
Professor Reiko Tanaka
Profile: https://profiles.imperial.ac.uk/r.tanaka
Contact details: r.tanaka@imperial.ac.uk
| Project title | Description |
| Mathematical modelling of microbial evolution and competition in eczema development | This project is suitable for student who want to gain following skills: 1) Mathematical modelling of biological systems. 2) Application of evolutionary theory to study host-microbe interactions. 3) Critical thinking and translation of biological data into a mathematical framework. 4) Computational modelling, data visualisation, and code organisation. Eczema is a chronic inflammatory skin disease that typically begins in infancy and is characterised by skin damage, inflammation, and microbial dysbiosis. Preventative treatments remain limited, partly due to a poor understanding of the mechanisms underpinning disease development. A key contributor to eczema development during infancy is Staphylococcus aureus (SA) – a pathogen that exacerbates disease symptoms by producing proinflammatory toxins through its accessory gene regulator (agr) system. A longitudinal study by Nakamura et al. found that infants who developed eczema maintained stable SA colonisation on their skin, while those who remained healthy showed a decline in SA populations. Intriguingly, many SA strains from healthy infants acquired mutations in the agr system. These agr-deficient strains were less able to colonise the skin and induce inflammation in murine models, yet they outcompeted more virulent SA strains in healthy infants. In this project, we investigate how SA strains evolve and compete on infant skin to uncover mechanisms that could guide the development of preventative treatment strategies for eczema. This project aims to develop and analyse a mathematical model that can explain how SA strains evolve and compete on infant skin to drive eczema development. This aim will be achieved through the following objectives: 1) Build an ordinary differential equation (ODE) model of SA strain dynamics by adapting an existing mathematical framework that captures microbial mutations. 2) Explore how host-microbe and microbe-microbe interactions affect SA strain composition. 3) Interpret results based on findings from Nakamura et al. and the wider literature. |
| Mathematical modelling of human microbiome using relative abundance data | This project is suitable for students who want to gain the following skills: 1) Mathematical modelling of ecological dynamics. 2) Experimental design using simulation studies. 3) Computational modelling, data visualisation, and code organisation. The human microbiome plays a critical role in maintaining health by regulating immunity, metabolism, and resistance to pathogens. Disruptions to the microbiome have been linked to a wide range of diseases. However, our mechanistic understanding of how microbes interact to drive community composition and function remains poor, hindering our ability to design treatment interventions targeting the microbiome. Mathematical models, particularly generalised Lotka-Volterra (gLV) models, are promising for inferring microbial interactions. By capturing how microbes grow and influence one another over time, gLV models can reveal whether microbes cooperate or compete with one another, provided that absolute microbial abundance data (e.g., microbial biomass per species) are available. However, most microbiome datasets are of relative microbial abundances, which capture microbial proportions rather than absolute counts. This compositional nature can misrepresent inferred microbial interaction networks. For example, Joseph et al. (2020) showed that variations in total microbial biomass can lead to qualitatively incorrect inferences, such as misclassifying cooperation as competition. Conversely, Remien et al. (2021) found that if total microbial biomass remains constant, relative abundance data may still recover qualitatively correct inferences. However, how biomass variation impacts the accuracy of gLV-inferred interactions from relative abundance data remains poorly understood. This project aims to develop gLV models to investigate microbial interactions within the human microbiome, using both relative and absolute microbial abundance data. We will first explore how total biomass fluctuations influence the accuracy of inferred interaction networks. Then, we will propose strategies for integrating targeted biomass measurements into experimental workflows to strengthen the reliability of inferred interactions from relative abundance data. To address this aim, we propose the following objectives: 1) Simulate synthetic absolute abundance data under biologically realistic biomass trajectories (e.g., antibiotic-induced biomass change, recolonisation, oscillatory variations, and environmental fluctuations). 2) Investigate inference error when fitting gLV models to relative vs absolute abundance data, focusing on qualitative accuracy (e.g., misclassifying cooperation as competition). 3) Identify conditions where inference reliability breaks down. 4) Investigate how collecting sparse biomass measurements at specific time points (e.g., at perturbation events) can help strengthen the reliability of inferred interactions when combined with relative abundance data. 5) Propose guidelines for when and how to sample biomass measurements in future experiments. |
| Extracting Patient-Centered Environmental and Treatment Factors from Text using Large Language Models for Eczema Causal Forecasting | Atopic Dermatitis (AD), commonly known as eczema, is a chronic inflammatory skin disease characterised by intense itching and recurrent eczematous lesions. Its pathogenesis is multifactorial, involving complex interactions between genetic predispositions, environmental triggers, and adherence to treatment regimens. A significant portion of patient experiences, particularly regarding daily activities, symptom fluctuations, and specific treatment responses, continues to be documented by clinicians or patients in tables or text formats such as diaries, messages, questionnaires, and interviews. These qualitative narratives contain rich, context-specific insights about symptom triggers and treatment efficacy that are often overlooked in conventional data analysis pipelines. Recent advancements in Large Language Models (LLMs), exemplified by models like GPT-4 and LLaMA-3, have revolutionised natural language processing by demonstrating capabilities in contextual understanding and structured information extraction from diverse text sources. These models present an opportunity to automatically extract structured, causally relevant features from raw patient narratives and reports. When synergistically combined with existing predictive models, such as EczemaPred, this approach could facilitate the development of a patient-centric system that can forecast how eczema symptoms may change over time. This project aims to enable causal forecasting of eczema symptoms to identify which treatments work best for individual patients and which environmental factors most strongly influence their symptoms. By doing so, we can move beyond population-level associations and toward personalised care, where interventions are tailored to each patient’s unique context, lifestyle, and symptom trajectory. |
Professor Rylie Green
Profile: https://profiles.imperial.ac.uk/rylie.green
Contact details: rylie.green@imperial.ac.uk
| Project title | Description |
| Living Bionics: Stimulation to drive neural network development | Electrical stimulation has been demonstrated to induce directional neurite growth in various cell types, both human and non-human using biphasic stimulation. This research project aims to evaluate a range of sinusoid stimulation frequencies to drive activity, growth and release of neurotransmitters of developing neurons using a cell stimulation rig made in house. |
| Spinal cord bridge | Nerve regeneration in an injured spinal cord is often restricted. One possible reason may be the lack of topographical signals from the material constructs to provide contact guidance to invading cells or re-growing axons. This research project aims to evaluate electroactive scaffolds and study device topographical effects on neural and glial cell behavior. |
| In-Ear EEG - signal detection | Implementing signal processing and ML for detection of abnormal brain activity in TBI when monitored through in-ear electrodes |
| Injectable electrodes: Colloidal systems for conductive nanoelectronics | This project is about biomaterials development to make injectable systems that are electrically addressable |
| Implanted device development for targeted in-tumour delivery of chemotherapeutics | Design and prototyping of device for delivery of electronic chemotherapy. Understanding fabrication and drug loading interactions and subsequent impact on delivery into brain tumour tissues. |
Dr Sophie Morse
Profile: https://profiles.imperial.ac.uk/sophie.morse11
Contact details: sophie.morse11@imperial.ac.uk
| Project title | Description |
| Can focused ultrasound delay Alzheimer's disease? | Focused ultrasound is a technology that has very recently shown to restore cognitive function in Alzheimer's disease patients. This is a non-invasive technology that can be focused onto specific regions of the brain. One theory is that this technology can restore cognition by stimulating the innate immune cells of the brain as well as neuronal function and health. In this project you will explore 1) whether this same technology can be used to delay Alzheimer's as well as restore cognition and 2) delve into exploring the mechanisms behind why these effects are observed. This will involve working with mouse brain tissue, sectioning, imaging, staining and fluorescence microscopy. |
| Can focused ultrasound delay brain ageing? | Focused ultrasound is a technology that has very recently shown to restore cognitive function in Alzheimer’s disease mice and patients. This is a non-invasive technology that can be focused onto specific regions of the brain. One theory is that this technology can restore cognition by stimulating the innate immune cells of the brain as well as neuronal function and health. In this project you will explore 1) whether this technology can be used to delay age-related cognitive decline, as well as restore cognition in aged mice and Alzheimer’s disease, and 2) delve into exploring the mechanisms behind why these effects are observed. This will involve working with mouse brain tissue, sectioning, imaging, staining, fluorescence microscopy and molecular techniques such qPRC and ELISA. |
| FPGA-based driving circuit for therapeutic ultrasound array | Therapeutic focused ultrasound applications from neuromodulation, blood-brain barrier opening to histotripsy place demanding and often conflicting requirements on driving electronics: multi-channel phase and amplitude control, precise waveform synthesis, high-voltage output, and low-latency programmability across a wide frequency range. Commercial systems rarely expose the full parameter space necessary for research-grade sonication protocol development, motivating the need for purpose-built, open-architecture driving platforms. This project will design and implement an FPGA-based multichannel driving system for a therapeutic ultrasound phased array, targeting frequency range relevant to transcranial and in vivo neuromodulation applications. The student will develop HDL-based waveform generation logic supporting independent per-channel phase, frequency, and amplitude control; design high-voltage switching and impedance-matching front-end circuitry compatible with piezoelectric transducer arrays; and validate system performance through electrical characterisation and hydrophone-based acoustic field mapping. The completed system will serve as a programmable research platform for the broader ultrasound neuromodulation programme, enabling rapid iteration over sonication parameter spaces. The project requires basic knowledge in digital hardware design (VHDL/Verilog), power electronics and RF circuit design. |
| Neuromodulation of Memory Circuits in Mice | We seek an MRes student to support investigating into how focused ultrasound can modulate memory-relevant neural circuits in mice. This role combines behavioral neuroscience, surgical technique, and neuroimaging—ideal for someone interested in translational neuromodulation research. Memory formation depends on astrocyte signalling in the hippocampus. We use two-photon calcium imaging during spatial memory tasks to monitor astrocyte activity, focused ultrasound neuromodulation (PhoCUS system/iTBUS) to selectively modulate circuits, and behavioral analysis to link neural dynamics to learning. The MRes student will help conduct stereotactic surgeries, run behavioral experiments on the T-maze with trained mice, support ultrasound neuromodulation sessions, and contribute to experimental design and data interpretation. We seek candidates with strong interest in neuroscience or neuroengineering, manual dexterity for surgical work, and a collaborative, problem-solving approach to research. Pre-requisite: Personal Animal License |
| Developing an algorithm to quantify how therapeutic ultrasound can modulate the brain's immune cells | Glia are essential for the brain to function properly and they are also heavily involved when the brain does not function properly. These glial cells are involved in the immune response of the brain, clearing unwanted substances and regulating neuronal activity. We are investigating how therapeutic ultrasound can be used as a tool to modulate the behaviour of these cells with the potential to help treat brain diseases, such as brain tumours, Alzheimer’s disease and Parkinson’s disease. We have developed an algorithm to quantify changes in the morphology of these glial cells in an automated way - which is saving so many hours of work. In this project, we want to take it a step further. Can we also quantify images of human brain tissue? What about multiple markers of glial cell activity at the same time? Can we automate the segmentation of white and grey matter regions? This project will be on the computation image processing side of the work, however, you can also shadow and/or get experience with the actual staining and imaging of mouse brain tissue to see how the images are acquired. Ultimately, with this project you will enabling the powerful ability of ultrasound, as a therapeutic technology to modulate cellular activity in the brain, to be taken a step further. Good programming/image processing skills needed. |