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University of Oxford

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PhD in Artificial Intelligence for Musculoskeletal Digital Twins and Real-World Health Data University of Oxford in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Competition funded PhD project. The student will receive training and supervision in a world-class environment, with financial support available for travel to conferences. No stipend amount or tuition details are 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
Data Science
Epidemiology
Information Technology
Biology
Artificial Intelligence
Health Disparities
Medical Statistics
Medical Science
Digital Twin Technology
Salud Pública
Multimodal Imaging
Emergency Response
Real-world Data
Statistics

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About this position

PhD opportunity at the University of Oxford in Artificial Intelligence for musculoskeletal digital twins, focused on real-world health data, clinical AI, health equity, and musculoskeletal health.

The project is based at the Botnar Research Centre, within NDORMS and the Centre for Statistics in Medicine, University of Oxford. It sits in the Planetary Health Informatics Lab and explores how AI and large health datasets can be used to build an MSK Clinical AI Digital Twin that models the patient journey from home and primary care to hospital, surgery, and rehabilitation.

Research directions include fairness and performance of MSK AI across population groups, robustness of open models in emergency and low-resource settings, multi-modal imaging (X-ray, CT, MRI), and reproducible equity-aware evaluation of clinical AI. The student will work with electronic medical records, imaging, radiology, gait lab data, UK Biobank, CPRD, and other international databases, using epidemiological, statistical, and artificial intelligence methods.

This is a 3-year DPhil project. The post is competition funded and includes training, supervision, access to high-performance computing, and support for conference travel. The post is open to students worldwide.

Eligibility: a first or upper second-class BSc degree (or equivalent) in a relevant subject, plus English language competence where applicable.

How to apply: contact the relevant supervisor(s) or the Graduate Studies Office first, then apply to the DPhil in Clinical Epidemiology and Medical Statistics using course code RD_NNRA1. Applications open mid-September and the deadline is 2026-12-01.

Funding details

Competition funded PhD project. The student will receive training and supervision in a world-class environment, with financial support available for travel to conferences. No stipend amount or tuition details are 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. English language competence is required where applicable. Applicants interested in an MSc (Research) should contact the Graduate Studies Office.

How to apply

Contact the relevant supervisor(s) or the Graduate Studies Office first for advice on essential requirements. Then apply online to the DPhil in Clinical Epidemiology and Medical Statistics using course code RD_NNRA1. Applications open mid-September and the deadline is 1 December at 12:00.

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