Reliable forecasts of multiple sclerosis progression using conformal prediction

Develop conformal-prediction methods for reliable forecasts of multiple sclerosis progression using clinical, MRI and wearable data, with neurologists. Supervisor: Nery Riquelme Granada.

Relevant quantitative 2:1 degree; probability/statistics and substantial Python work; healthcare interest and postgraduate-research English requirements. MSc/ML, uncertainty quantification or clinical-data experience desirable. Apply through the research portal to PhD Computer Science (7089), selecting 2027/28 and naming the supervisor. Include research proposal, CV, two academic references, transcripts/certificates and English evidence if applicable.

Competition-based studentships typically cover UK-level tuition fees and a living stipend for top-ranked applicants; funding is allocated on a rolling basis.

Fresh official recruitment evidence reviewed on 26 October 2026. Specific project identity, current application window, research scope, eligibility, funding limitations and application route reviewed together. Funding competition does not guarantee an award for every advertised project.