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 1 or 2.  If you are applying in round 3, 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 2024-25

Dr David Labonte

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Title Description
3D shape analysis via flow fields Any two objects may differ in size and in shape. Size differences are typically obvious, but shape differences can be subtle, and challenging to quantify, because typical methods involve 2D measures such as characteristic lengths. Recent advances in computational methods have enabled detailed quantitative analyses on complex 3D shapes. In this project, you will learn and deploy one approach, based on a shape atlas. This approach is applicable to any set of 3D objects, and thus has substantial generality.Generating a shape atlas requires to estimate an anatomical model (i.e. template) as the mean of a set of input shapes. Subsequently, shape variation is quantified via the calculation of the deformation required to mold this mean shape onto a shape representing an individual from the population of relevant shapes. Mathematically, this approach represents deformation between shapes as the diffeomorphic transformation of flow fields. By means of a gradient descent optimisation scheme, the method is able to produce a statistical atlas of the population of shapes, so allowing both quantitative and detailed visual comparison of shape differences across objects.You will use this approach to investigate differences in shape across workers of a colony formed by social insects - leaf-cutter ants. Workers of this important pest species differ in weight by more than two orders of magnitude, and it is commonly speculated that this size-differences goes hand in hand with a task specialisation, leading to the definition of "worker castes". However, it remains entirely unclear whether such shape differences exist. The required workflow is established in the laboratory, and µCT scans of ant workers of different sizes are available. Your main task will be to process these scans using CT-segmentation software and Blender, to then deploy the developed workflow, and produce the shape Atlas, which will enable you to answer if ant workers of different castes differ only in size or also in shape. Recommended literature:Toussaint, N., Redhead, Y., Vidal-García, M., Lo Vercio, L., Liu, W., Fisher, E. M., ... & Green, J. B. (2021). A landmark-free morphometrics pipeline for high-resolution phenotyping: application to a mouse model of Down syndrome. Development, 148(18), dev188631.

Dr Juan Alvaro Gallego

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Title Description
On the universality of neural network models of motor behaviour Artificial neural networks have emerged as powerful models in computational neuroscience: not only can they perform many laboratory tasks that we study in animals but, more intriguingly, the activity they produce during these “behaviours” is remarkably similar to that of their biological counterparts (e.g., [1,2]).
A network’s activity is influenced by a number of variables, including its architecture and how the model is trained. Thus, an important question at the intersection of neuroscience and artificial intelligence research is how different classes of models perform the same task. Previous studies have tried to understand how different neural network architectures perform a common series of cognitive tasks [3], but this question remains mostly unexplored in the case of motor tasks —motor tasks are quite different in that movements are continuous variables that need to be controlled by the brain, rather than discrete, often categorical quantities that need to be selected.
Your goal in this project is to train a variety of models to perform a complex motor task in which monkeys reached to a series of targets that were presented during movement execution [4] and compare the dynamics produced by the different types of networks. Since brains arguably evolved to control movement [5], establishing the universality –or lack of– of the solutions found by different networks performing the same motor tasks would be an important contribution to neuroscience. Moreover, identifying any similarities and differences across classes of models is crucial to understand how much they can tell us about biological brains.

[1] Sussillo et al Nature Neurosci 18:1025–33, 2015
[2] Yang et al Nature Neurosci 22:297–306, 2019
[3] Maheswaranathan, Williams et al NeurIPS 2019
[4] Glaser et al. Nature Communications 9(1):1–14, 2018
[5] Wolpert et al Trends Cogn Sci 5:487–94, 2001
Understanding the neural basis for different types of learning Animals including humans can rapidly adjust their behaviour in the presence of perturbations. These includes counteracting mechanical perturbations to the limb [1] (such as when running into water), or distorted feedback about the state of the limb [2] (such as when reaching into a pond). Yet, despite considerable effort, the neural basis for this rapid learning or motor adaptation remains largely elusive.
Recent theoretical and computational advances in understanding the coordinated activity of neural populations [3] are helping to cast light into this fascinating problem. For example, our group has shown that, in contrast to learning a new skill through substantial practice, this form of learning may not need “plastic changes” in the brain [4,5]. Moreover, while motor regions of the brain seem to be key for counteracting mechanical perturbations [4,6], learning to adapt to distorted feedback may be driven by higher brain regions [6]. In this project, you will systematically compare these two forms of adaptation by multi-area neural population recordings from monkeys performing these two classic experiments on different days (data from [3]). These analyses will include understanding changes in neural dynamics as animals plan and execute these movements, as well as a comparison between motor regions of the brain. Your results will shed light in the neural basis for rapid learning and also inform the field of brain-computer interfaces, which necessitates more flexible “decoding algorithms”.

[1] Shadmehr and Mussa-Ivaldi. J Neurosci 1990
[2] Krakauer et al. Nature Neurosci 1999
[3] Gallego et al. Neuron 2017
[4] Perich, Gallego & Miller. Neuron 2018
[5] Feulner et al. Nature Communications 2022
[6] Sun, O’Shea et al Nature 2022

Dr Lance Rane

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Title Description
Data synthesis for wearables-based motion systems: Musculoskeletal modelling At our spinout Biomex ( we use motion data in real time to infer gait characteristics for the guidance of therapy. However, data are often much easier to obtain from healthy controls than from patients. Can techniques of data synthesis be used to make up the shortfall?One issue with data from body-worn sensors is that data augmentation - a crucial technique for successful application of machine learning - can be very difficult. How would the data change if the subject were a little taller, or heavier, or walked a little faster? These questions are difficult because the relationships in each case are complex and non-linear. They are best answered by the use of musculoskeletal models - full mathematical descriptions of the human body in motion. This project aims to use data from IMUs to feed existing musculoskeletal models to produce simulations of the original human motion. In model-space, perturbations may be applied - eg to walking speed - and the simulation re-run to regenerate the sensor data. This provides an infinite data generator, through a combination of tweaks to important parameters - such as speed, leg length, height etc. Final evaluation of performance will be made by using the resulting data to train existing neural network classifiers, and testing their performance against baseline.
Data synthesis for wearables-based motion systems: Generative modelling At our spinout Biomex ( we use motion data in real time to infer gait characteristics for the guidance of therapy. However, data are often much easier to obtain from healthy controls than from patients. Can techniques of data synthesis be used to make up the shortfall?In this project, students will use existing motion datasets from healthy controls and patients. Applying advanced generative modelling techniques (variational autoencoder, GAN, diffusion modelling) they will seek to create representations of the data that disentangle pathology features. This will enable the simulation of pathology in healthy subjects, and vice-versa, providing a potent new method of performing artificial data synthesis. It could also provide new insight into the characterisation of movement disorders and musculoskeletal conditions that affect movement. Final evaluation will be made by using the newly generated data to re-train and test existing neural network classifier models.

Dr Majid Taghavi

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Title Description
Multimodal haptic device Haptic stimulation is an innovative technology that leverages the sense of touch to convey information to users. Traditionally, this has been achieved through vibrations to deliver notifications. However, in this project, our aim is to develop a novel wearable technology capable of delivering a diverse range of subtle mechanical stimulations, including tapping and sliding, to the wearer. This device could be used for sending complex physical information to individuals with visual, hearing, or mobility impairments, providing cutaneous sensory re-education, or therapeutic guidance to the patient during an activity.
Interactive robotic skin  Human can examine structures or areas for size, shape, consistency, texture, and other characteristics simply by moving their fingers and palms over the surfaces. Dexterous movement, flexible and soft architecture, and a variety of sensing capabilities on the skin enable this palpation. In this project, we aim to advance this concept by developing a cutting-edge soft robotic material. This material will incorporate embedded electroactive artificial muscles and tactile sensors, creating an interactive composite working like a robotic skin. By employing this innovative technology, it will be possible to scan the body and accurately detect variations in stiffness, opening up new possibilities for medical diagnostics and interventions
Soft-rigid hybrid robotic material  Soft robotics built from highly compliant materials suggests safe and adaptable technologies for many applications including human-machine interactions (e.g. surgical robots, implants, and wearables). In practical uses, it is usually needed to integrate dissimilar materials, such as soft with rigid and electrodes with non-conductors, in the architecture of soft robots for applying mechanical support and electrical connections. Inspired by nature, where dissimilar materials are found with progressive compositional changing (e.g. muscle-tendon-bone), in this research we will develop a novel robotic material with a smooth transition from soft to rigid, embedding a unique capability of interacting with the environment. This soft-rigid hybrid muscle will benefit from a reliable connection to rigid bodies, withstanding diverse mechanical deformation and robust electrical connections.
Variable stiffness soft robotic material Variable stiffness materials provide a core technology for wearables and assistive robotics. They are a class of smart materials with the capability of stiffness change under external stimulation. Within this project, we will develop different types of electroactive ionic elastomers for variable stiffness applications. The intrinsic mechanical properties of the ionic elastomer and their response to the applied electric field will also be characterised. We will then design a proof of concept robotic component to demonstrate its use in wearables and implants.
Nylon thread artificial muscle Coiled polymer artificial muscles offer a new type of thermo-active soft actuation technology with high potential to be used in wearables and assistive robotics. A twisted and coiled sewing thread such as silver-coated Nylon can generate large stroke and high-power density when actuated by ohmic heating. They however suffer from the lack of high bandwidth controllability because of the slow cooling process. In this project, we aim to tackle this challenge by implementing a multi-physical approach and introducing intelligent structures to extend their applicability in interactive textile technologies.  
Social soft robot Social robots are robots that interact and communicate with humans, usually to provide some kind of social or service function. They are becoming increasingly common in settings such as healthcare, education, and the home. In this project, you will develop a new type of social robot that is made of soft material and can change its shape and characteristics using soft actuators to communicate with people. For example, the robot could change its shape to communicate different emotions, such as smiles, anger, or fear. 

Dr Reiko Tanaka

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Title Description
Systems biology approach for mechanistic understanding of paediatric asthma exacerbations  Asthma is the most common chronic disease of childhood, affecting up to 10% of children in Westernised societies and 200,000,000 individuals worldwide. Many factors indicate the importance of the microbiome in asthma. Asthma is rare in rural societies, and its prevalence has been increasing markedly in the developing world as populations become urbanised. Exacerbations of asthma are often precipitated by otherwise trivial viral infections. Our studies have shown that the normal human airways contain a characteristic microbiome that is altered in children and adults with the illness. Asthmatic airways contain an excess of pathogens (which may damage the airways) and also lack particular commensal species that may be necessary for normal airway functions. This project will take a systems biology approach, by combining experiments with primary bronchial epithelial cells, in silico modelling, and clinical data analysis, to elucidate the effects of the airway bacterial microbiome in asthma, and the role of epithelium barrier integrity in disease initiation and control. We already have a preliminary mathematical model that will be used to quantify the dynamic interactions among pathogen, commensals at the airway surface, the airway barrier and the immune system, preliminary data from in vitro experiments, and clinical data to be analysed. The student(s) will conduct several computational methods to identify the model structures and model parameters. 
Systems biology approach for cancer immunotherapy: Dynamical mechanisms to turn hot tumours into cold tumours Immunotherapy is a promising treatment for cancers. Several immunotherapy treatments have already achieved clinical success. For example, checkpoint inhibitors (CPIs) targeting PD1 and CTLA4 achieved up to 11% of cure rates in advanced melanoma. However, its treatment response rates remain low for a majority of cancers. This project aims to apply a systems biology approach to increase the efficacy of CPI therapies. CPI therapy achieves cancer regression by preventing exhaustion of T cells that pre-exists in the tumours but does not exhibit strong antitumour immunity if tumours have few immune cells. One way to improve the CPI efficacy is to convert immune-excluded cold tumours into immune-infiltrated hot tumours, by enhancing infiltration of tumour-specific immune cells into the tumour tissue. Ishihara et al have recently engineered a tumour-homing anti-tumour cytokine IL-12 (Nat Biomed Eng 2020) that turns immunologically cold tumours into hot ones, and demonstrated a high antitumor efficacy of a combination therapy using IL-12 and CPI for cold tumors. However, the actual dynamical mechanisms by which IL-12 converts cold tumours to hot tumours and how IL-12 acts in combination with CPI therapy is still elusive. This project aims to investigate the cellular mechanism of immune cells infiltration during the combined therapy. The student will develop and analyse a computational model of immune cells infiltration based on the results from in vitro and in vivo experiments, to suggest the best timing of adding CPI theraty to achieve the most anti-tumour efficacy.
Learning from noisy labels by EczemaNet Eczema is the most common form of skin disease. The eczema severity is currently assessed by trained clinical staff. However, the subjective nature of assessing these disease signs could be a source of significant inter and intra-observer variability. Our group recently developed EczemaNet, a prototype novel computer vision pipeline for automated evaluation of eczema severity from camera images (Pan, Hurault et al. 2020). EczemaNet uses a CNN that first detects areas of AD from camera images and then makes probabilistic predictions of the severity of seven clinical signs of AD. EczemaNet was trained using the camera images with eczema severity scores provided by trained clinical staff. However, the severity scores may have inter-rater variability, resulting in noisy labels. This project aims to further improve EczemaNet by achieving a better generalisation capability in the presence of noisy labels.  The student is expected to review, implement, and validate the image analysis pipeline using off-the-shelf deep-learning and image analysis techniques and software packages to improve the prediction accuracy in the presence of noisy labels.
Automatic quantification of fungal burdens in histology images using deep neural networks Invasive fungal infections are commonly treated by antifungal drugs, which are under the growing threat of antifungal resistance. New antifungal drugs that overcome antifungal resistance are being sought actively. Effectiveness of new antifungal drugs is often tested in vitro by comparing fungal growth rates estimated from time-course optical density (OD) data of fungal burden in different treatment conditions. However, OD is an indirect measure of fungal burden, with potential bias in the evaluated growth rates and artefacts in the data such as multiple scattering. Various methods to improve bacterial growth rate estimation from OD have been proposed and subsequently been made available to the wider bacterial community. However, these methods require converting OD to cell counts in a process called calibration, which is not routinely available for fungi. 

Dr Shlomi Haar Millo

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Title Description
Behavioural Biomarkers for Deep brain stimulation Deep brain stimulation (DBS) is a routine treatment for patients with Parkinson's disease (PD) which improves their motor symptoms and as a result their function and quality of life. While DBS is an effective therapy, it is still not clear how and why it works and therefore there are many open questions as to how it can work better. This research project is part of a program that aims for a better understanding of the effects of DBS on the system level (in addition to improving symptoms) and a search for biomarkers to improve DBS delivery. In this research project, you will help to collect behavioural (movement sensor) and neural (EEG) recordings from PD patients with implanted DBS electrodes. You will analyze the behavioural data to study the effects of the DBS parameters on their body movement, and develop behavioural digital biomarkers to improve DBS delivery.
Motion tracking in the home for Dementia care The UK DRI Care Research & Technology centre, works to create new technologies that will deliver the highest quality dementia care in the home. We develop a range of devices that allow us to track a person’s behaviour and health at home, to understand an individual’s behaviour and predict when problems might arise. In this project we will explore the potential of using camera based 3D skeletal body tracking for continuous monitoring in the home. You will work on setting an of-the-self depth camera with single-board computer (e.g. Raspberry Pi) and skeletal tracking SDK as a standalone device that can work in the home and record body movement. You will then test and validate the data quality and reliability during daily life activities in our living lab. This is a domestic studio flat with passive movement sensors, providing a bridge to research ‘in the wild’. Lastly, you will work with our software engineers to integrate this data stream into our healthy home database. 
Motor control fingerprints of neurological conditions In the UK DRI Care Research and Technology Centre we are collecting a multimodal dataset of movement, brain activity, physiology and cognition from a diverse cohort of participants with neurological conditions (including dementia, Parkinson’s disease, motor neurone disease, and traumatic brain injury). This is done in our Living Lab which is a domestic studio flat with passive movement sensors, providing a bridge to research ‘in the wild’. In this project, you will support the data collection effort (and will get to work with the different neurological patient groups), and lead a sub-project of the data analysis. This will look into defining, coding, and analysing specific features of the subjects’ movements which are motor control fingerprints of neurological conditions. This will be done both with a data-driven ML approach and in a clinically guided hypothesis-driven approach. 
Real-World Motor Learning in Embodied Virtual Reality A key challenge in neuroscience, neurology and neurorehabilitation is to measure and train motor control and learning in free-behaving real-life tasks. We recently demonstrated the feasibility of studying real-world neuroscience using wearable technologies and data-driven approaches to uncover neural mechanisms of learning. We also developed an embodied virtual-reality (EVR) setup, which allows us to study motor control and learning in a controlled-real-world learning environment. In this project, you will use our EVR setup to induce perturbations aimed to manipulate motor learning mechanisms. You will record subjects’ movement (with body sensor networks) and brain activity (with mobile EEG) while performing a motor learning task with visual perturbations in the VR. This will force subjects to use different learning mechanisms and in your analysis, you will work to map the behavioural changes induced by the perturbations and changes in the brain activity.

Dr Sophie Morse

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Title Description
Do immune cells enter the brain when delivering drugs across the blood-brain barrier with focused ultrasound? Focused ultrasound and microbubbles is the only technique that can non-invasively, locally and temporarily open the blood-brain barrier to allow drugs into the brain. The way this technology works is by first injecting clinically-approved microbubbles and drugs into the bloodstream. When the microbubbles reach the area where the ultrasound is targeted in the brain, these bubbles oscillate, mechanically stimulating the blood vessels, allowing the barrier to open so that drugs can get reach the brain. This technology has generated a lot of excitement as the blood-brain barrier currently prevents over 98% of drugs from entering the brain. Therefore, this technique can allow drugs that have previously failed clinical trials due to this barrier, to be tested again. But do immune cells also infiltrate from the blood into the brain, together with the drug, while the blood-brain barrier is open? In some situations immune cell infiltration would want to be avoided, for safety reasons. However, in other cases, such as when treating brain tumours, the presence of peripheral immune cells inside tumours could really help. Our own immune cells are actually best suited at tackling the complex heterogeneity of brain tumours. In this project, you will explore with sectioning, staining and fluorescence microscopy whether different types of immune cells are extravasating into the brain when the blood-brain barrier is opened with focused ultrasound. Ultimately, the aim is to find out with which ultrasound parameters you would get immune cells entering the brain, to either choose parameters for a safer drug delivery profile without immune cell presence, or to harness the power of our own immune cells to treat brain tumours.
Can focused ultrasound stimulate the activity of the brains innate immune cells?  Microglia are the innate immune cells of our brain. They actively survey the brain and clear away toxins and pathogens. The ability to temporarily stimulate microglia has the potential to help treat brain diseases, such as brain tumours, Alzheimers disease and Parkinsons disease. For example, stimulating microglia can help clear away amyloid-beta plaques that accumulate in Alzheimers disease brains.  Focused ultrasound is a non-invasive technique that has been used to stimulate neurons. However, we have discovered that the activity of microglia can also be modulated depending on the way ultrasound is emitted. In this project, the level of microglia activation will be assessed for a variety of different ultrasound exposure parameters. Staining for various markers of microglia and their activation will be used to visualise (with microscopy) changes in their morphology and activation level. This project will lead to key advances in the unexplored territory of how the activity of the innate immune cells in our brain can be non-invasively controlled with ultrasound. 
Can focused ultrasound stimulate astrocytes in our brain?  Astrocytes, together with endothelial cells, are the gatekeepers of the brains main security system “ the blood-brain barrier. They control which substances enter and exit the brain, and keep harmful substances out. Astrocytes also regulate blood flow to transport nutrients to neurons depending on their energy needs. These cells are at the forefront of our thought processes: they regulate our synapses, and recycle and secrete neurotransmitters. The ability to temporarily stimulate astrocytes has the potential to help treat brain diseases, such as Alzheimers disease and Parkinsons disease. Focused ultrasound is a non-invasive and targeted technique that has been used to stimulate neurons. However, we have discovered that the activity of astrocytes can also be modulated depending on the way ultrasound is emitted. In this project, the level of astrocyte activation will be assessed for a variety of different ultrasound exposure parameters. Staining for various markers of astrocytes and their activation will be used to visualise (with microscopy) changes in their morphology and activation level. This project will lead to key advances in the unexplored territory of how the activity of the brains gatekeepers can be non-invasively modulated with ultrasound to treat brain diseases. 
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 same technology can be used to delay age-related cognitive decline, as well as restore cognition in 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 and fluorescence microscopy.  

Professor Anil Bharath

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 exist. This problem is not fully addressed in the AI field, where it is often assumed that data are readily available with ground truth, and that the (data, label) tuple can be made sufficient. 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 radical ones require new approaches to learning, such as **contrastive learning**. This project will investigate contrastive learning in the context of medical imaging data from a variety of sources.

Professor Holger Krapp

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Title Description
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 this mode sensing hypothesis requires a comparative study of the receptive field properties of motion sensitive interneurons in the visual systems of dipteran flies other than blowflies as well as species that belong to other orders of flying insects. Hoverflies, which show distinctly different flight patterns than blowflies and 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 a few of those interneurons have been studied so far. This project requires the dissection of flying insects for extracellular recordings from motion sensitive interneurons during visual stimulation. 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. The principles underlying state change estimates in flying insects will inform the design of energy-efficient flight control architectures for autonomous aerial systems.  

Professor Rylie Green

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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 randomly oriented or aligned collagen fibers coated on cuff electrodes to study device topographical effects on astrocyte behavior and neurite outgrowth respectively, using electrical regimes. 
Biofunctionalising electrodes through conductive hydrogel coatings Peripheral nerves cuffs can have there biointegration improved through the use of conductive hydrogel coatings. One of our long-term goals is to improve the long term performance of these electrodes. This projects will investigate reducing inflammation at the site of implantation by the incorporation and release of anti-inflammatory mediators from the hydrogel coatings. Different mediators will be investigated along with different release methods and how these impact the performance of the cuff electrodes. 
A bioelectronic implant for cancer treatment This project revolves around aiding in the development of a device for the selective delivery of chemotherapy directly to the site of non-operable brain tumors (glioblastoma multiforme). This device consists of a conductive polymer-based material that can used as an electrically controlled drug delivery system. The goal of this project is to evaluate the drug release profiles for multiple different molecules that are analogs to those commonly used in chemotherapy. Parameters such as molecule size, charge, and stability will be investigated. Characterization of the drug release profiles will be accomplished through chemical, electrochemical, and spectroscopic techniques. 
In ear monitoring of EEG This project is a funded collaboration with EEE and international partners. It seeks to deliver accurate EEG and other biosignal information through eletronics mounted in an ear bud. The project will focus on the design and characterisation of dry wearable electrodes and the identification of biosignals through processing. The areas of focus will span biomaterials, electronics, signal processing and neural netoworks.