PhD Studentship: Machine Learning and Computational Modelling to Define the Abnormal Heart Tissue Responsible for Fatal Heart Rhythms

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 hosp...

Opportunity details

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 creat...

Eligibility

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.

Requirements

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.

Funding and benefits

Funding eligibility: UK Students. Funding amount: £21,805 (2026/2027). 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.

How to apply

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.

Open the official application information

Verify the deadline and final conditions on the official provider page before submitting an application.