PhD Research Studentships

Reduced-order modelling for predicting atrial fibrillation burden and ablation outcome

Supervisors: Caroline RONEY and Wen WANG
Apply by:15 June 2026
Start in:September (Semester 1)

Description

Atrial fibrillation (AF) affects over 1.5 million people in the UK and almost 50 million worldwide, and is a leading cause of stroke and reduced quality of life. Catheter ablation is the most effective rhythm-control therapy, but long-term success rates for persistent AF remain around 50–60%, and predicting which patients will benefit, and which ablation strategy will best reduce AF burden, remains a major clinical challenge. Patient-specific bi-atrial electrophysiological digital twins offer mechanistic insight, but full-physics simulations are too slow for clinical timescales and cannot be continuously updated as new patient data arrive. This PhD project aims to develop a reduced-order modelling framework that delivers fast predictions of post-ablation AF burden and recurrence, and refines them as more clinical and wearable data become available. The student will build patient-specific Gaussian Process Emulators with Bayesian history matching, trained on quasi-Monte Carlo samples of high-fidelity bi-atrial biophysical simulations, and coupled to Ensemble Kalman Filtering for sequential Bayesian updating of personalised posteriors. Complementary strategies will be benchmarked against this baseline: physics-informed neural operators for full-field outputs such as 3D atrial wall-stress and fibrosis distributions, and latent neural ODEs for capturing AF burden trajectories across follow-up. Synthetic fibrosis generation via diffusion models will expand training cohorts where clinical data are limited. The result will be a digital-twin framework for personalised prediction of ablation outcome and long-term AF burden.

You’ll work as part of the Personalised Cardiac Modelling Lab (https://pcmlab.co.uk/).

Funding

Funded by: SEMS
Home Fees applicants only

Eligibility

  • The minimum requirement for this studentship opportunity is a good honours degree (minimum 2(i) honours or equivalent) or MSc/MRes in a relevant discipline.
  • If English is not your first language, you will require a valid English certificate equivalent to IELTS 6.5+ overall with a minimum score of minimum score of 6.0 in each of Writing, Listening, Reading and Speaking).
  • Note for EPSRC studentships; these studentships are open to those with Home fee status; according to the EPSRC terms and conditions.
  • Candidates are expected to start in September (Semester 1).

Contact

For informal enquiries about this opportunity, please contact Caroline RONEY or Wen WANG.

Apply

Start an application for this studentship and for entry onto the PhD FT Medical Engineering full-time programme (Semester 1 / September start):

Apply Now »

Please be sure to quote the reference "SEMS-PHD-732" to associate your application with this studentship opportunity.

Related website:https://pcmlab.co.uk/
Keywords:Artificial Intelligence, Machine Learning, Biomedical Engineering, Electrical Engineering, Electronic Engineering, Cardiology