Phd In Machine Learning–Driven Cardiac Imaging

Heart failure with preserved ejection fraction (HFpEF) represents a growing clinical challenge, particularly among patients with type 2 diabetes (T2D). Early myocardial remodelling, including changes in fibre alignment, sheetlet orientation and extracellular matrix composition, precedes overt dysfunction detectable by conventional imaging. Diffusion MRI offers a non‑invasive window into these microstructural changes by measuring parameters such as mean diffusivity (MD), fractional anisotropy (FA), helix angle (HA) and secondary eigenvector angle (E2A).

This fully funded PhD studentship, hosted in the Department of Cardiovascular Sciences at the University of Leicester, seeks to leverage advanced machine learning models to detect and predict these subtle alterations in diabetic and HFpEF cohorts. The successful candidate will work closely with supervisors Dr Maryam Afzali, Professor Huiyu Zhou and Professor Gerry McCann, gaining expertise in cardiac MRI acquisition, image processing, statistical learning and biomedical signal interpretation.

The project involves optimising diffusion MRI acquisition protocols and developing robust post‑processing pipelines to extract quantitative microstructure metrics from large datasets. The student will apply and potentially develop machine learning algorithms (e.g., deep learning, sparse coding, manifold learning) to identify patterns associated with early diabetic cardiomyopathy and to predict progression to clinically significant HFpEF.

Training will be provided through the university’s Graduate School, including courses in research methods, biomedical imaging, and data science. The student is expected to present findings at national and international conferences and to publish in peer‑reviewed journals. The position includes a stipend covering living expenses and tuition fees, in line with UKRI‑funded PhD studentships.

Applicants should hold at least an upper second‑class honours degree (or equivalent) in a relevant discipline such as biomedical sciences, physics, computer science, engineering or a related field. Experience with MRI data processing, programming (Python/Matlab) or machine learning is advantageous but not essential, as full training will be provided. The successful candidate will join a vibrant interdisciplinary research environment and benefit from the university’s state‑of‑the‑art imaging facilities.

To apply, candidates must submit an online application via the University of Leicester postgraduate research portal, including a CV, cover letter outlining motivation and relevant experience, and the contact details of two academic referees. Informal enquiries can be directed to the lead supervisor, Dr Maryam Afzali, via email. The application deadline is 28 June 2026, with an anticipated start date of 21 September 2026.