PhD Position in Physics-Informed Machine Learning for Cardiovascular Medicine

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

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. One promising approach is the use of digital twins—virtual models that replicate a patient’s heart using mathematical equations describing cardiac physiology and clinical data (e.g., ECGs, MRI scans). However, creating reliable digital twins remains difficult, limiting their clinical use.

In this project, you will build on our past work designing Physics-Informed Machine Learning (PIML) for cardiac digital twins. PIML is a set of new techniques that combine artificial intelligence’s (AI) ability to learn from data with mathematical descriptions. PIMLs can learn from small amounts of data and are more immune to hallucinations than conventional AI, making them exceptionally suited for biomedical applications.

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. You will develop cutting-edge PIML methods with real-world applications in cardiovascular medicine, gaining valuable expertise in research at the intersection of AI, healthcare, and mathematical modelling.

What is offered:

The Scholarship includes:

• A competitive annual bursary for 3 years (£21,237/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, please click the 'Apply' button, above, and fill out the application on the University portal. 

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: 30 May 2025 (or until the position is filled)
• Preferred start date: July 2025 (later start dates can be considered)

Advert information