About this position
Sudden cardiac arrest (SCA) accounts for approximately 15-20% of reported deaths in the UK. Mostly, these deaths are linked to cardiac arrhythmias. One of the most common and dangerous is Ventricular Tachycardia (VT), a rapid and unstable heart rhythm that often arises from regions of diseased or scarred heart tissue.
VT can lead directly to sudden cardiac death if not treated immediately.
VT can be treated with a procedure called ‘catheter ablation’, which significantly reduces VT recurrence rates, reduces hospitalisations, and improves survival compared with medication alone. The goal of VT ablation is conceptually simple: find where the fatal heart rhythm is originating and use either thermal or electrical energy to destroy, or ablate, that tissue, leaving the rest of the heart to function normally. However, procedural success rates of catheter ablation are modest.
Improving the precision and effectiveness of VT ablation is therefore a major unmet clinical challenge.
This project is an interdisciplinary program that brings together Warwick Medical School and partners from the School of Engineering and Industry, combining machine learning and computational modelling to solve a cardiovascular medicine problem. The central aim is to develop next-generation computational approaches to identify the electrical signatures of diseased cardiac tissue and improve the targeting of VT ablation procedures.
During ablation, wires are introduced into the heart to examine the electrical properties of the tissue. These wires collect local electrical signals, known as an electrogram (EGM), at 1000s of locations in the heart. Currently, the EGMs are then described in relatively simple terms, for example, by their maximum amplitude or timing, and these features are then used to create a map of the heart to guide where to ablate.
We call this electroanatomical mapping, and it is central to modern-day VT ablation. However, compressing these signals into 1 or 2 descriptive features means we are using only a fraction of the data we collect.
In this project, we will therefore seek to use advanced mathematical and machine learning approaches to identify hidden information within cardiac electrical signals that is currently unavailable to clinicians.
To unravel the hidden information, the student will apply advanced computational approaches to large-scale clinical datasets collected during VT ablation procedures at University Hospitals Coventry and Warwickshire NHS Trust, an internationally recognised centre for ventricular arrhythmia management. The project will involve analysing thousands of EGMs recorded directly from human hearts and developing new methods to extract clinically meaningful information from them. Potential approaches include advanced signal decomposition and feature extraction, time–frequency and spectral analysis, entropy-based metrics, graph representations of cardiac conduction, and supervised, unsupervised, and deep learning approaches for classification of abnormal electrical activity within the heart.
The project offers opportunities to work across multiple disciplines, including:
- Machine learning and artificial intelligence
- Biomedical signal processing
- Computational modelling
- Clinical electrophysiology
The student will join a highly collaborative environment involving clinicians, engineers, mathematicians, and computer scientists, with access to unique clinical datasets and state-of-the-art mapping technologies.
This PhD would particularly suit candidates with backgrounds in:
- Artificial intelligence or machine learning
- Computer science
- Data science or computational modelling
- Engineering
- Mathematics or applied mathematics
- Physics
Funding Details
The award will cover the UK (home) tuition fee plus an annual stipend at the UKRI rate - £21,805 (2026/2027), for 3.5 years of full-time study and a one-off research training grant.
£21,805 (2026/2027)
Funding
GBP 21,805 per year
How to apply
- Read the full advert on the source site — it carries the authoritative terms.
- Prepare your SOP, CV, transcripts and referees before the deadline.
- Apply through the university's own portal. Never pay a fee to a third party.