Xujiong Ye

[£20,780 per year stipend, payment of Home tuition fees, and a Research Training Support Grant of £5,000 over 3.5 years.] This PhD studentship at the University of Exeter offers an exciting opportunity to contribute to the development of artificial intelligence (AI) and machine learning (ML) methods for the early detection of diabetic retinopathy and neuropathy using advanced medical imaging techniques. Diabetic Retinopathy (DR) is a leading cause of blindness among working-aged individuals, and current screening rates are insufficient, especially in lower-middle income countries. The project aims to address these challenges by automating the analysis of retinal fundus, OCTA, and CCM images to detect not only diabetic retinopathy but also glaucoma, cataract, and age-related macular degeneration (AMD). As a PhD student, you will work with large and diverse datasets, including the EyePACS dataset (over 5 million retinal images), UK BioBank fundus dataset (175,824 images), INSIGHT data from NHS Trusts (over 35 million images), HVDROPDB dataset from international collaborations, and data from North Devon and Royal Devon University Healthcare NHS Foundation Trust. The project will involve developing and evaluating AI, ML, deep learning, and data science approaches for medical image analysis, with a focus on autonomous grading of fundus images and explainable AI to ensure clinical interpretability. The supervisory team includes Professor Neil Vaughan and Professor Xujiong Ye, supported by experts in medical image analysis and NHS ophthalmology. The research will compare the grading accuracy of ML algorithms with NHS clinicians, building on Exeter’s previous work that demonstrated the potential of ML and DL systems to outperform human graders in sensitivity. The ultimate goal is to provide evidence for the safe clinical adoption of these technologies, improving screening rates, reducing undetected cases, and lowering healthcare costs. Funding for this studentship includes a stipend of £20,780 per year, payment of Home tuition fees, and a Research Training Support Grant of £5,000 over 3.5 years. Applicants should have a strong background in a relevant discipline such as computer science, engineering, mathematics, medical science, or related fields, and an interest or experience in AI, ML, or medical image analysis. International applicants may need to provide proof of English language proficiency. The application deadline is 12 January 2025. Interested candidates should apply via the University of Exeter application portal and direct any project-specific enquiries to Professor Neil Vaughan at [email protected]. This is a unique opportunity to join a leading research group and make a significant impact on the future of medical diagnostics and patient care.

[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.