[School of Engineering PhD Scholarships] Self-supervising Heart AI to Detect Obstruction Without Stress

About the project

About the Project Hypertrophic cardiomyopathy (HCM) is the most common inherited heart disease, affecting 1 in 500 people. In many patients, the thickened heart muscle intermittently blocks blood flow out of the heart but – in a proportion of patients (up to 35%) - does so only under conditions of physical exertion. Detecting this "latent obstruction" currently demands expensive, specialist stress tests, so many patients go undiagnosed until symptoms become severe. This diagnostic problem of latent obstruction comes up at least once per week in the clinic. This fully funded PhD asks a bold question: can AI spot the hidden fingerprint of obstruction in a heart that is simply at rest? You will build novel self-supervised deep learning models that turn beating-heart MRI videos into compact, clinically meaningful "signatures" of shape and motion and then test whether these signatures can flag latent obstruction without a single stress test. Working together with a leading clinical cardiology team, you will help design and grow a unique prospective clinical imaging cohort, giving you privileged access to data no public dataset can offer. Why this project stands out: • Given genuine co-supervision by a Clinical Professor in inherited cardiac conditions (ICC) and an AI-for-Medicine engineering academic, you will be a true bridge between two disciplines/worlds, not simply a visitor in either. • The ICC service in Manchester is one of the largest and busiest in Britain with more than 1000 HCM patients under care. This population represents a mixture of those with and without obstruction. Thus, we have sufficient patient data to allow for the training, validation and testing of AI models in this area of work. • You will work at the frontier of self-supervised representation learning and explainable AI, applied to a real, unmet clinical need. • Direct pathway to clinical translation: your work could reduce reliance on costly stress testing and support treatment decisions for HCM patients. • You will be embedded in both a hospital-facing clinical research environment and a computational AI research group, with access to high-performance computing and clinical imaging facilities. Ideal candidates are those who are from computer science, engineering, physics, or biomedical/data science backgrounds with an interest in healthcare impact. No prior clinical background required, full training provided. If you want your PhD to sit at the genuine interface of AI innovation and patient care, we want to hear from you. This project is expected to start in September 2027. Before you apply: We strongly recommend that you contact the supervisors for this project before you apply. How to apply: To be considered for this project you must complete a formal application through our online application portal. If you already have an applicant account this link will directly open an application for PhD School of Engineering Scholarships . If you don’t already have an applicant account, please follow the instructions here . When applying, please specify the full title and supervisor/s of the project, details of your previous study, and names and contact details of two referees. You must also upload a Supporting Statement describing the motivation to apply to the project, your CV and transcripts of awarded and in-progress university qualifications . Please note late or incomplete applications will not be considered. Equality, diversity and inclusion are fundamental to the success of The University of Manchester and central to all our activities. A diverse research community strengthens creativity, productivity and quality, while increasing the societal and economic impact of our work. We welcome applicants from all career paths, backgrounds and sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation or transgender status. We welcome applications from candidates returning to study after a career break or experience in other roles. Flexible study arrangements may be available, including part-time study at 50%, 60% or 80%, subject to the requirements of the project and funder. Eligibility : The standard academic entry requirement for this PhD is an upper second-class (2:1) honours degree in Computer Science (or international equivalent) OR any upper-second class (2:1) honours degree and a Master’s degree at merit in Computer Science (or international equivalent). Previous research experience in AI development for medicine is desirable. This project will remain open until filled. If your application is submitted by 1 st November 2026, you can expect a decision by 18 th December 2026. If your application is submitted by 15 th January 2027, you can expect a decision by 30 th March 2027. Self or externally funded students can also be considered for this project. FSESoE

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