PhD Position in Physics-Aware Machine Learning for Cardiovascular Medicine

Funding for: UK/Home Students

Funding amount: Annual tax-free stipend of £22,780/year, full coverage of tuition fees for UK/Home Students, plus training/travel funds

We invite applications for a fully funded PhD research studentship in Physics-Informed Machine Learning for Cardiovascular Medicine. This opportunity is open to UK (Home) candidates only.

Project Overview

Arrhythmias are disorders of the heart’s electrical activity, often caused by complex changes in heart tissue. Understanding and treating arrhythmias effectively remains a major challenge.

Recent advances in artificial intelligence (AI) offer new avenues to tackle this problem. AI models have demonstrated strong potential in clinically relevant insights from electrical signals such as ECGs, and from cardiac imaging modalities including MRI, echocardiography, and CT.

Another promising approach is the use of cardiac digital twins—mathematical models that simulate a patient’s heart to allow the design and in silico testing of novel treatments. To date, there are few techniques that integrate AI and digital twins to improve patient outcomes.

Your Role

In this project, you will develop new methods that combine AI and digital twins for cardiovascular applications. You will build on our group’s expertise on Physics-Informed Machine Learning (PIML), a powerful approach that combines data-driven AI with the rigour of physical and physiological models. PIML can learn from small amounts of data and are more immune to hallucinations than conventional AI, making them exceptionally suited for biomedical applications.

Research Environment

You will integrate the exciting environment of the recently merged City St George’s University of London, and a vibrant multidisciplinary team of scientists, engineers, and clinicians. This project offers an exceptional opportunity to conduct cutting-edge research at the intersection of machine learning, healthcare, and computational modelling, contributing to real-world clinical impact.

What is offered

The Scholarship includes:

• A competitive annual bursary for 3 years (£22,780/year)
• Full tuition fees for UK/Home Students.
• The opportunity to earn up to £4,300/year through a non-compulsory teaching assistantship
• Funding to participate in conferences and training to support your professional development

Eligibility

The studentships will be awarded based on outstanding academic achievement and the potential to produce cutting-edge research. Prospective applicants must:

• Hold a good honours degree in Physics, Maths, Engineering, or a related discipline.
• Be familiar with differential equations and have some programming experience (preferably in Python).

If English is not your first language, provide evidence of proficiency through:

• A minimum IELTS average score of 6.5 and a minimum of 6.0 in each component. OR
• A master’s degree awarded in an English-speaking country and taught in English.

How to apply

For informal inquiries about the project, contact Dr Marta Varela (mvarela@citystgeorges.ac.uk) or Dr Giacomo Tarroni (giacomo.tarroni@citystgeorges.ac.uk).

To apply, fill out the application on the University portal via the above ‘Apply’ button. Please add only ‘Physics-Informed Machine Learning for Cardiovascular Medicine’ in the research proposal box. Questions regarding the application portal should be directed to pgr.sst.enquire@city.ac.uk.

Please also email the following documents to Dr Varela:

1. A CV detailing academic qualifications, research experience, and relevant technical skills
2. A cover letter explaining your motivation for applying, relevant experience, and how your skills align with the project.

• • Application deadline: 15 September 2025 (or until the position is filled)
  • Start date: October 2025

Advert information