Application deadline: 16 June 2025 16:00 GMT
Atrial fibrillation (AF) is the most common heart rhythm disorder, but understanding its causes is challenging due to its complexity. It involves triggers like abnormal electrical signals (focal triggers) and swirling patterns of electrical activity (re-entrant rotors), which interact across the heart’s inner and outer layers. Because most studies to date focus on just one layer, understanding what keeps AF going is challenging.
This PhD project aims to bridge that gap by combining advanced machine learning tools with a new experimental protocol developed by University Hospital Sussex in collaboration with their industrial partner, and which makes it possible to collect data from both layers of the heart at the same time.
Key aims of this project are:
- to analyse dual-layer heart data to study how the inner and outer layers interact. In particular, we will focus on dissociation (when the layers do not synchronize), and investigate how it contributes to AF perpetuation (or maintenance/persistence);
- to build ML models that include the heart’s physical properties to find patterns in the data and
- predict which areas in the heart drive AF.
This project will explore important questions such as: Are the patterns that drive AF stable or do they change over time? How do the heart’s layers interact during AF? Can stimulating the heart during normal rhythm help predict problem areas during AF?
Successful completion of this project will provide new insights into AF that could improve treatments, such as ablation, where small parts of the heart are treated to restore and maintain normal rhythm.
Although no medical background will be assumed, the successful applicant will have the unique opportunity to combine analytical and experimental work, through interacting with both academics at University of Sussex (particularly Sussex AI) and clinicians from University Hospitals Sussex, with technical support from industry.
Eligibility: Applicants should have/expect to have at least a 2:1 undergraduate honours degree in a relevant subject and meet our English language requirements. They should have a strong background in physics and/or mathematics (e.g., PDE, optimization) and/or machine learning (applied to spatiotemporal data). International and UK applicants are both eligible to apply.
Sponsor: This scholarship is funded by the UK Engineering and Physical Sciences Research Council (EPSRC)
As an EPSRC student you will join a vibrant doctoral community and will benefit from:
- 3.5 years of funding with a a tax free living allowance at the standard Research Council rate – currently £20,780 in 2024-5 – and international or UK PhD fees
- An option of 3 months of additional funding to cover a placement outside academia
- Funding for training and research expenses, such as conference trips and experimental costs
- Supervision by world-leading researchers
- Our Entrepreneurship Summer School and Responsible Research and Innovation workshops tailored to the needs of engineering and physical sciences researchers.
- Our Researcher Development workshops and access to taught modules relevant to your project
For questions regarding the project, please contact the Project Supervisor:
l.berthouze@sussex.ac.uk.
For questions relating to the online application process, please contact enginf-pgr@sussex.ac.uk
3.5 years of funding with a tax free living allowance at the standard Research Council rate, currently £20780 in 2024-5, & international or UK PhD fees; an option of 3 months of additional funding to cover a placement outside academia
