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Neil Vaughan

4 days ago

PhD Studentship: AI and Image Processing for Diabetic Retinopathy (DR) Smart Cameras University of Exeter in United Kingdom

Degree Level

PhD

Field of study

Biomedical Engineering

Funding

Available

Deadline

Dec 8, 2026

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Country

United Kingdom

University

University of Exeter

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Keywords

Biomedical Engineering
Medical Science
Retinal Disease

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Posted Yesterday

About this position

[Fees plus a stipend of £21,805 per annum for 2026/7; funded by EPSRC (Engineering and Physical Sciences Research Council).]

PhD studentship at the University of Exeter on AI and Image Processing for Diabetic Retinopathy (DR) Smart Cameras. This project sits at the intersection of medical image analysis, machine learning, deep learning, and ophthalmic diagnostics, with a strong clinical motivation: diabetic retinopathy remains a major cause of blindness, and large numbers of patients worldwide do not receive regular annual screening.

The project aims to develop and evaluate AI methods for analysing retinal images to detect and grade diabetic retinopathy, as well as related eye diseases such as glaucoma, cataract, and age-related macular degeneration (AMD). In addition to standard retinal fundus imaging, the research will explore newer image modalities including OCTA and CCM images, with further application to diabetic neuropathy diagnosis.

The work will focus on building autonomous grading systems for large-scale retinal datasets, using approximately 500,000 fundus images from open-source and customised datasets. The project also emphasises explainable AI, so that diagnostic outputs and model decisions are interpretable for clinicians and patients. A further aim is to compare the performance of machine learning-based grading with NHS clinician grading, supporting evidence for safe clinical use.

The studentship is supported by established supervision from Professor Neil Vaughan and Professor Xujiong Ye, together with input from medical image analysis labs and wider clinical and external partners. The project has access to exceptionally large datasets, including EyePACS, UK Biobank fundus images, INSIGHT data from NHS Trusts, HVDROPDB data from rural India collaborations, and North Devon / Royal Devon University Healthcare NHS Foundation Trust data.

Funding is provided by EPSRC and includes fees plus a stipend of £21,805 per annum for 2026/7. The advert is a PhD studentship rather than a postdoctoral or master’s opportunity. Applicants with a background or strong interest in AI, machine learning, computer vision, image processing, biomedical engineering, or medical data science should be well aligned with the project.

The application deadline is 2026-12-08. Interested candidates should apply via the Exeter funding page using the official application link and check the university page for any additional instructions or supporting documents required.

Funding details

Available

What's required

Applicants should have a strong interest in AI, machine learning, deep learning, data science, and medical image analysis. The project would suit candidates with skills or experience in computer vision, image processing, or healthcare data analysis. The advert does not specify a required degree classification, but a relevant background in computing, engineering, biomedical engineering, or a related quantitative subject is expected. Experience with explainable AI, retinal imaging, or clinical data would be advantageous.

How to apply

Apply through the University of Exeter funding application link. Submit your application before 8 December 2026. Review the advertised project details carefully and follow any additional instructions on the funding page.

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Professors

Professor at University of Exeter

University Name
Country flag

United Kingdom

Research Interests

Statistics

Artificial Intelligence

Retinal Disease

Medical Science

Data Science

Professor at University of Exeter

University Name
Country flag

United Kingdom

Research Interests

Statistics

Artificial Intelligence

Retinal Disease

Medical Science

Peripheral Neuropathy