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University of Exeter

PhD Studentship: R2T2-Med: Resource-Aware AI for Scalable and Deployable Medical Image Analysis University of Exeter in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Available

Deadline

Dec 8, 2026

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Country

United Kingdom

University

University of Exeter

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Keywords

Computer Science
Biomedical Engineering
Deep Learning
Artificial Intelligence
Computer Vision
Medical Science
Magnetic Resonance Imaging
3d Imaging

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About this position

[Funded studentship with fees covered and a stipend of £21,805 per annum for 2026/7.]

PhD studentship at the University of Exeter for research on R2T2-Med: Resource-Aware AI for Scalable and Deployable Medical Image Analysis.

This project addresses a major challenge in modern healthcare AI: medical image analysis models can be accurate, but often require high computational power, memory, and processing time. That makes them difficult to scale to population-level research and expensive or impractical to deploy in clinical settings with limited hardware or connectivity. The studentship aims to develop a resource-aware AI framework for both 2D and 3D medical imaging that can adapt its computational demands to the complexity and uncertainty of each case, the clinical risk, and the available hardware.

The research will investigate where unnecessary computation occurs in current medical imaging models and how it can be reduced without losing clinically important information. It will then develop a flexible model family that can be trained once and configured for different resource budgets. A major part of the project is ensuring that efficiency improvements do not disproportionately affect particular anatomies, clinical sites, or patient groups, with safety and subgroup fairness embedded throughout the work.

The project will be validated in two complementary settings: population-scale abdominal MRI analysis using UK Biobank and near-patient dermatology imaging using public and established clinical data pathways. Expected outcomes include an adaptive and deployable medical imaging AI framework, reproducible efficiency benchmarks, reusable software, and evidence from both large-scale and resource-constrained environments. The project aims to reduce processing time and memory requirements while maintaining accuracy, reliability, and subgroup safety.

Funding is provided by the Engineering and Physical Sciences Research Council (EPSRC) Doctoral Landscape Award. The studentship includes fees plus a stipend of £21,805 per annum for 2026/7.

Deadline: 08 December 2026.

Funding details

Available

How to apply

Apply through the University of Exeter funding/application link provided. Submit the application before 2026-12-08. Review the linked award page for any additional required documents or eligibility steps.

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