Mathematical Modelling of Haemostatic Data to Predict Clinical and Therapeutic Outcomes University of Nottingham in United Kingdom

This fully funded PhD project at the University of Nottingham offers an exciting opportunity to advance research in haemostasis through the integration of mathematical and computational modelling with laboratory work. The aim is to develop new approaches for targeting anti- and pro-thrombotic therapies in a personalised manner, leveraging data-driven techniques and experimental validation. Students will join a collaborative network with a strong track record in postgraduate supervision, ensuring comprehensive support and training throughout the studentship.

The research will involve hands-on laboratory techniques such as thrombin generation assays, alongside computational modelling, model validation, and advanced data analytics. Candidates will gain expertise in integrating computational models with experimental data, as well as machine learning and statistical skills for analysing large, multi-variate datasets. The project is highly interdisciplinary, providing exposure to clinical conditions including diabetes and inherited bleeding disorders.

Applicants should have a background in applied mathematics or a related discipline, or a biosciences/biomedical background with an interest in modelling and data analysis. Experience or interest in laboratory work is desirable. The position is open to home, EU, and international applicants, though international recruitment is capped at 30% of the cohort due to funder stipulations.

Funding is provided by the Medical Research Council for 4 years, covering a stipend, tuition fees (home and international rates), laptop allowance, research training and support grant (£5,000 per annum), and travel allowance (£300 per annum). The supervisory team includes Professor John King (University of Nottingham), Neil Morgan (University of Birmingham), and Alison Goodall (University of Leicester), offering expertise across mathematical sciences and cardiovascular research.

To apply, review the eligibility criteria and submit your application via the MRC AIM website before the deadline of January 9, 2026. For project-specific enquiries, contact Professor John King at [email protected]. This studentship provides a unique opportunity to develop cross-disciplinary skills and contribute to impactful research in clinical and therapeutic outcomes.