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University of Oxford

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PhD in Learning Prognostic World Models from Longitudinal Medical Data University of Oxford in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Competition funded PhD project (students worldwide). Limited financial support is mentioned for conference attendance; no stipend amount is stated in the post.

Deadline

Dec 1, 2026

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Country

United Kingdom

University

University of Oxford

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Keywords

Computer Science
Biomedical Engineering
Medical Imaging
Biology
Artificial Intelligence
Medical Statistics
Medical Science
Salud Pública
Clinical Prediction
Statistics

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About this position

PhD opportunity at the University of Oxford in Learning Prognostic World Models from Longitudinal Medical Data (Botnar-2026-07), based at the Botnar Research Centre within NDORMS / CSM.

This project sits at the intersection of artificial intelligence, computer science, medical statistics, medical imaging, and clinical prediction. The research aims to build a multimodal, intervention-aware world model that learns patient-specific disease dynamics from serial imaging, reports, and structured clinical metadata. The focus is on prognostic forecasting, calibrated uncertainty, scenario-based simulation, and robustness across datasets, scanners, institutions, and subgroups.

Potential methods include multimodal encoders, JEPA-inspired temporal prediction, action-conditioned latent dynamics, and generative decoders for simulated future images. The study will use retrospective longitudinal cohorts with repeated imaging and external validation where possible, with the Osteoarthritis Initiative highlighted as an initial case study.

Funding: competition funded PhD project for students worldwide. The post mentions limited financial support for conference attendance, but no stipend amount is given.

Eligibility: applicants should have, or expect to obtain, a first or upper second-class BSc degree or equivalent in a relevant subject, and must provide evidence of English language competence where applicable.

Supervisors: Dr Paula Dhiman and Dr Rafael Pinedo-Villanueva.

Deadline: 1 December 2026, 12:00. Applications open mid-September. The DPhil is expected to commence in October 2027.

How to apply: contact the supervisors to express interest, then apply to the D.Phil in Clinical Epidemiology and Medical Statistics (course code RD_NNRA1) through Oxford graduate admissions. The official application guide is available on the Oxford admissions website.

Funding details

Competition funded PhD project (students worldwide). Limited financial support is mentioned for conference attendance; no stipend amount is stated in the post.

What's required

Applicants should have, or expect to obtain, a first or upper second-class BSc degree or equivalent in a relevant subject. Evidence of English language competence is required where applicable. Applicants should contact the relevant supervisor(s) and may need to consult the departmental Education Team for programme-specific essential requirements.

How to apply

Contact the relevant supervisor(s) to register interest in the project. If needed, email the departmental Education Team at [email protected] for guidance on essential requirements and the official application process. Submit an application to the D.Phil in Clinical Epidemiology and Medical Statistics using course code RD_NNRA1 via the Oxford graduate admissions portal.

More information can be found here

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