This PhD studentship at University College London focuses on the application of artificial intelligence (AI) to ocular imaging and oculomics, leveraging the retina as a window into both neurological and systemic health. The project is based in the Department of Mechanical Engineering and is part of Cohort 3 of the Centre for Doctoral Training in Digital Health Technologies. Students will benefit from a collaborative, interdisciplinary environment, including annual doctoral conferences, clinical shadowing, and training workshops in ethics, entrepreneurship, and patient/public engagement.

The research aims to develop machine learning (ML) methods for automated analysis of retinal images, particularly ultrawide field pictures, to identify risk factors and predict responses to treatment for vision-threatening complications of systemic diseases such as diabetes. The project will also explore the identification of novel retinal biomarkers for broader systemic conditions, including cardiovascular and neurodegenerative diseases. A key aspect is the development of explainable AI approaches to classify images and elucidate the features driving predictions, using a large dataset of thousands of labelled patient images.

Beyond disease-specific analysis, the studentship offers opportunities to contribute to future research in novel instrumentation and software for ophthalmic vision health, with potential impact on neurovascular and neurodegenerative disease diagnostics. The CDT provides a rich training environment, including partner sandpits for idea development and a 3-month secondment to gain practical experience.

Funding for this position is co-provided by tech4health CDT and Moorfields Eye Hospital BRC, covering tuition fees at the home rate, a generous stipend (£24,466 for 2026/27), and a research training support grant for consumables and conference costs. International students are eligible, with additional tuition fees covered internally at UCL. The CDT particularly welcomes home applicants.

Applicants should demonstrate high academic achievement in a relevant field, strong programming and AI skills, interest in statistical methods, and proficiency in English. Experience with image processing and analysis is desirable. The application requires a CV, cover letter, academic transcripts, and contact details for two academic referees, to be sent to CDT Manager Jamie Kozak by 2nd March 2026. Interviews are expected to take place by mid-March.

For more details and to apply, visit the project page or contact the CDT Manager directly.