The MRC funded projects for the current call are listed below. The application form will ask for one project to be selected or two projects to be ranked in order of preference.

Projects open for application

Adaptive learning for closed-loop neuromuscular control / 22 Oct 2025

Lead supervisor: Professor Anil Bharath
Co-supervisor: Professor Anthony Bull
Deadline for applications: 23:59 (BST) 22 Oct 2025

Tuition fees: Will be covered at the UKRI rate (currently £5,006) and international candidates will be required to cover the remaining fees. International tuition fees are currently £31,100 per annum in the Faculty of Engineering.

Project title: Adaptive learning for closed-loop neuromuscular control 

Project abstract: This project is a collaboration with Biomex, an Imperial spinout developing novel wearable aids for rehabilitation in patients with sports injury and osteoarthritis. Biomex’s devices apply functional electrical stimulation to muscles of the patient in response to a real time stream of movement data, enabling the clinical team to exert control over movement and provide a training signal to guide the patient. The patient’s neuromuscular system adapts to the muscle contractions caused by electrical stimulation in ways that are complex and difficult to predict.

This project aims to advance our understanding of neuromuscular control and learning through the development of a next generation prototype device that adapts control on the fly in order to improve the training signal. Patient and device here represent two learning systems, the interaction of which must be modelled and accounted for in order to optimally guide the rehab process.

Applications are particularly encouraged from candidates who have practical experience with the use of open-source tools for data science or machine learning.

 

Objectives:

Year 1

  • Literature review.
  • Prototyping of learning algorithms.
  • Development and validation of symmetry criterion and/or other trajectory optimality criterion.

Year 2

  • Continued development of learning algorithms.
  • Development of computational infrastructure (data processing and training pipeline, wireless transfer protocols, custom hardware if required).

Year 3

  • System refinement; trials in patients undergoing rehabilitation.
  • The student will have access to all Graduate School training opportunities and teaching roles.

Previous projects

Environmental impacts and optimisation of prostate cancer pathway / deadline 26 Aug 2025

Lead supervisor: Professor Andrea Rockall
Co-supervisor: Dr Onesmus Mwabonje
Deadline for applications: 23:59 (BST) 26 Aug 2025

Note: Tuition fees will be covered at the UKRI rate (currently £5,006) and International candidates will be required to cover the remaining fees. International tuition fees are currently £45,850 per annum (in the Faculty of Medicine).
 
Project abstract: Greenhouse gas emissions in healthcare cause 5% of global emissions. Establishing sustainable diagnostic pathways that enhance early cancer diagnosis is key to emissions reduction. Using prostate cancer as a use case, working with Siemens, this project will apply Life Cycle Assessment (LCA) and costing (LCC) approaches. A framework for improving the sustainability of diagnostic pathways will be developed, potentially reducing emissions and conserving resources whilst maintaining high standards of patient care. 

Objectives:

  1. Comprehensive LCA/LCC of prostate cancer pathways (scope 1, 2 and3).
  2. Identify key stages in the diagnostic process with significant environmental impacts and compare with existing alternatives.
  3. Develop strategies to reduce the environmental footprint of these pathways, including optimising patient appointment scheduling, scanning protocols, one-stop diagnostic clinics, virtual/online clinics and patient travel routes.
  4. To assess the environmental impact and consider the mitigations available from of incorporating AI workflow/AI embedded in MR and low-field MR.

Y1: Review of literature; define current pathways. Develop LCA/LCC models for different aspects of the pathway (imaging, biopsy, consultations, initial treatment and follow-up) using existing databases. Advanced LCA training provided.

Y2:  Model different mitigation strategies; impact of potential early detection (e.g. through screening programme).

Y3: Minimum 3 month work experience with Siemens in circularity, energy consumption & low carbon MRI. Design potential roadmap for prostate cancer screening programme in UK with MRI-on-wheels to serve regions to reduce patient travel. 

Y4: 6 month write up and dissemination.

 

Balancing the knee: From the native ligaments to the development of a medical device / deadline 30 June 2025
Lead supervisor: Dr Angela Kedgley
Co-supervisor: Professor Anthony Bull
Deadline for applications: 23:59 (BST) 30 June 2025
 
Project abstract: The stability and mobility of the knee depend on its ligaments, and while much research has focused on static or isolated ligament behaviour, little has targeted the role of the ligaments in natural, functional knee movements. The role of the ligaments is amplified following total knee arthroplasty (TKA), with pain, stiffness, instability, and even failure potentially resulting from an unbalanced joint. However, characterising the role of the ligaments in maintaining stability of the knee during dynamic movements has proved challenging to date.
 
Robotic systems provide an opportunity to simulate knee motion under loaded conditions and measure ligament forces in real-time, offering new insights into knee joint mechanics. This project aims to advance understanding of knee ligament mechanics and apply this knowledge to improve TKA outcomes. A SimVitro musculoskeletal simulator will be used to recreate knee movements, enabling quantification of ligament restraint forces, and to test a novel ligament balancing device designed for using during TKA surgery. The work will be conducted in collaboration with Smart Surgical Solutions.
 
This project offers the opportunity for a highly motivated PhD candidate to gain skills in robotic simulation of physiological joint motion and the design of surgical devices. The collaboration with Smart Surgical Solutions provides the prospect of a unique internship and the potential to make a meaningful impact on techniques used in joint replacement. Applicants should have a high calibre Master’s degree (or equivalent qualification) in engineering or physical sciences. Experience in programming using LabView or MATLAB is desirable. We are looking for highly motivated applicants with excellent interpersonal, written, and oral communication skills and an enthusiasm for innovation.
Next generation monoclonals for RSV / deadline 30 June 2025

Supervisor team: John Tregoning (Infectious Disease), Barbara Bravi (Maths), Paul Kellam (RQ Bio)

Deadline for applications: 23:59 (BST) 30 June 2025

Note: Tuition fees will be covered at the UKRI rate (currently £5,006) and International candidates will be required to cover the remaining fees. International tuition fees are currently £45,850 per annum (in the Faculty of Medicine).

Project abstract: Respiratory Syncytial Virus (RSV) causes a significant disease burden, particularly in the very young and very old. It is a very exciting time in RSV research; in the last few years there have been large steps forwards preventing infection by this virus, with the licensing of three vaccines, a monoclonal antibody (mAb) Nirsevimab (Beyfortus) with a further mAb Clesrovimab (MK-1654) under final regulatory review. A critical factor for the success or failure of both antibody and vaccines for RSV will be virus evolution and escape; but particularly this will impact monoclonal antibodies.

All licensed products to date target the RSV-F protein, a meta-stable protein, with two forms, pre- and post-fusion. RSV-F is relatively conserved, but can still evolve to escape monoclonal antibodies, this has been seen for the previously licensed monoclonal, palivizumab.

Aim: The aim of the project is to model potential RSV-F escape mutations and proactively develop monoclonal antibodies that target these variants effectively constraining the virus.

Plan of action – model existing antibodies to find best in class binding; develop novel approaches to improve binding to target antigen; explore mutational space for virus and anticipate future antibodies.

This work brings together the expertise of Imperial Department of Infectious Disease (DoID) in respiratory virology and immunology, Imperial Maths in AI and RQ Biotechnology an SME with a mission to develop medicines based on potent broad-spectrum monoclonal antibodies to provide instant and long-lasting protection for vulnerable people.

Year 1. Model interactions between antibodies and RSV-F

Year 2. Enhance binding efficiency of antibodies

Year 3. Use deep mutation sequencing to predict future escape mutants

Skills required – experience with bioinformatic models of protein-protein interactions and wet lab virology/ immunology.

Modelling strategies for the release of genetically-modified mosquitoes for arboviral control / deadline 31 July 2025

Lead supervisor: Dr Penny Hancock
Co-supervisor: Dr Lauren Cator
Industry partner: Biocentis, supervised by Dr Andrew Hammond

Deadline for applications: 23:59 (BST) 31 July 2025

Note: Tuition fees will be covered at the UKRI rate (currently £5,006) and International candidates will be required to cover the remaining fees. International tuition fees are currently £45,850 per annum (in the Faculty of Medicine).

 

Project abstract: This project explores how genetically modified mosquitoes could help stop deadly arboviruses such as dengue and Zika. Cutting-edge "genetic control" technologies can render entire mosquito populations infertile through the release of males engineered to carry sterile traits. Some approaches spread autonomously via gene drive, while others remain localized, each able to reduce the number of harmful mosquitoes without incurring wider ecological impacts. This interdisciplinary project aims to predict the potential impact of these approaches in real-world disease settings by developing mathematical models that consider not only the complex genetic modifications, but also how mosquitoes interact under different environmental conditions.

  • Partnering with genome engineers at Biocentis, we will analyse how modified mosquitoes survive, compete, and interact with their environment by altering conditions in the lab (Year 1).
  • Next, we will model and predict how natural environmental conditions could affect a genetic control program across diverse settings, from South American cities to African towns (Year 2).
  • Finally, we will integrate virus transmission data to evaluate genetically modified mosquitoes as a sustainable public health tool (Year 3).

In addressing these research questions, this project combines mathematical modelling with experimental ecology in a dynamic industry setting.

In vitro and ex vivo models to study pain and itch in keloid scars
Lead supervisor: Dr Claire Higgins
Co-supervisor: Dr Parastoo Hashemi

Note: Applications for this project will be reviewed on a rolling basis as and when submitted and the position will be filled as soon as a suitable candidate is identified. 

Project abstract: Keloids are a type of abnormal skin scar that outgrows the original injury site. In addition to this abnormal growth, they are associated with pain and itch which can often be more distressing to patients than the scar appearance. There is a genetic component to keloids, but we do not know if and how the genetics contributes to pain and itch perception. Pain and itch stimuli are detected by specialised sensory nerves that are present within the skin and the keloid itself, however the reasons for increased pain and itch in keloids remains unknown. It is unclear if there are alternations in the sensory neurons and how they perceive itch and pain stimuli, or whether differences in the milieu of the keloid lead to increases in stimuli.

  • In Yr1 of this PhD project, we will stain and image a multipanel of nerve subtypes in the skin and keloid samples to investigate how the nerve architecture and fibroblast architecture is altered in the keloid environment.
  • In Yr2 we will compare the effect of signalling from skin cells versus intrinsic genetics on nerve architecture and nerve subtypes in keloids.
  • Lastly, in Yr3 we will use an innervated skin model to assess the response of keloid and healthy skin models to itch and pain stimuli. All of the work will be conducted using ex vivo human tissue samples, or in cell culture using various assays including fibroblast culture, reprogramming to pluripotent stem cells and sensory neuron differentiation.

This PhD project is in collaboration with Monasterium, a company who specialises in development of ex vivo skin models for assessment of skin disease. The successful candidate will spend a minimum of 3 months at the industrial partners laboratories in Munster, Germany where they will be supervised by Dr Marta Bertolini.

Antimicrobial biomaterials for osteochondral regeneration / deadline 16 June 2025
Lead supervisor: Professor Julian Jones
Co-supervisor: Dr Andrew Edwards
Deadline for applications: 23:59 (BST) 16 June 2025
Note: Tuition fees will be covered at the UKRI rate (currently £5,006) and International candidates will be required to cover the remaining fees. International tuition fees are currently £29,900 per annum (in the Faculty of Engineering).
 
Project abstract: The aim is to produce a new 3D printed medical device that can not only regenerate cartilage damaged by trauma, but also prevent bacterial infection. Julian Jones’ team have developed, and patented, a new material that can guide bone marrow cells to produce high quality cartilage. 
 
The objectives of the PhD are to incorporated zinc ions into the scaffold for antimicrobial properties, and to work on the manufacturing process to make scaffolds for large cartilage defects. Testing of the antimicrobial properties will be carried out in the Institute of Infection. Orthox Ltd have licensed the patent and will collaborate on the additive manufacturing part of the project and train the student in technology transfer, such as good manufacturing practice and quality assurance of medical device production.
Methods for integration of sample-to-result lab-on-chip detection platform for molecular diagnostics / deadline 31 July 2025

Lead supervisor: Dr Jesus Rodriguez-Manzano
Co-supervisor: Professor Pantelis Georgiou
Deadline for applications: 23:59 (BST) 31 July 2025

Note: Tuition fees will be covered at the UKRI rate (currently £5,006) and International candidates will be required to cover the remaining fees. International tuition fees are currently £45,850 per annum (in the Faculty of Medicine).
 
Project abstract: Simple diagnostic questions such as differentiating serious bacterial infection from minor viral infection cannot be answered rapidly with the current standard of care. This leads to unnecessary hospital admissions, strain on NHS resources and overuse of antibiotics, which breeds antimicrobial resistance. New technology is needed to empower clinicians and benefit patients at different levels of healthcare.
 
Imperial and ProtonDx are developing sample-to-result rapid, accurate and truly portable testing technology for panels including respiratory infections and tropical diseases. We have pioneered a workflow relying on ultra-high purity sample extraction from nasal swabs, and electrochemical detection using microchip technology and AI, to be integrated as an automated platform called Lacewing.

In this project, the student will lead industrial research to transition the workflow into a sample-to-result cartridge. They will:

  1. Unlock bioreagent deposition at the surface of the microchip to provide specificity to a target and allow ambient temperature storage
  2. Implement algorithms for quality control, calibration and real-time nucleic acid detection
  3. Engineer the cartridge design in partnership with clinical collaborators
  4. Lead a study for yield and accuracy. 

This studentship will be uniquely led at the frontier of academia and industry, delivering innovative research for Imperial and a reliable easy-to-use platform towards commercialisation by ProtonDx. 
 

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