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Computational modelling of Cardiac Electrophysiology

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  • Full or part time
    Prof R Clayton
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

PhD opportunities are available in Computational modelling of Cardiac Electrophysiology, based in the Insigneo Institute for in-silico Medicine and Department of Computer Science, University of Sheffield, UK. These positions would suit applicants with a desire to undertake research training at the interface between Engineering and Medicine.

The heart is an electromechanical pump, and electrical activation of the heart acts to initiate contraction. In diseased hearts, the sequence of electrical activation is disturbed, and this can result in abnormal heart rhythms such as atrial fibrillation and ventricular tachycardia. The focus of this PhD project will be on building patient-specific anatomical models of the heart, and using these to target treatment of abnormal heart rhythm disorders. Your PhD will run alongside an EPSRC grant on Uncertainty Quantification in Prospective and Predictive Patient Specific Cardiac Models, which is a joint activity with King’s College London (see

PhD projects will involve working with clinical colleagues, processing medical images to generate anatomical models and processing signals recorded from patients to identify and extract specific features. Simulations will involve numerical solution of systems of ordinary and partial differential equations, requiring high-performance computing resources. Model analysis and personalisation will require techniques for parameter inference using statistical machine learning. Within this scope, the project can be tailored to the interest and technical strength of the candidate.

The Supervisor:
Richard Clayton has a degree in Applied Physics and Electronics from the University of Durham, and a PhD in Medical Physics from the University in Newcastle upon Tyne. He has been involved in research into cardiac arrhythmias for more than 20 year. He is a core member of the Insigneo institute for in-silico Medicine, and serves on the Insigneo board. For more information, please visit

Insigneo Institute for in-silico Medicine and Department of Computer Science:
The successful candidate will be located within Insigneo, which is a collaborative initiative between the University of Sheffield and Sheffield Teaching Hospitals NHS Foundation Trust. The Institute came into existence on 28 May 2012, distributing its charter and registering its first members. The Institute coordinates 140 academics and clinicians from a range of disciplines who collaborate to improve health outcomes by developing subject-specific computer models able to predict ‘biomarkers’ – measures of physiology that can support a clinical decision – which are difficult or impossible to obtain directly. These advanced computer simulations can then be used directly in clinical practice to improve diagnosis and optimise treatment, offering a path to a more personalised medicine.

The Department of Computer Science at the University of Sheffield was established in 1982 and has since attained an international reputation for its research and teaching. In the recent Research Excellence Framework (REF2014), 45% of the research in the department was recognised as internationally excellent in terms of originality, significance and rigour, and another 47% as internationally world leading. These results place the department among the top 5 UK Computer Science departments for research excellence.

Candidate Requirements:
Candidates are expected to have a solid mathematical background, with strong programming skills in at least one of Python, C/C++, and Matlab. Experience of numerical methods, image and signal processing, and statistical machine learning will be beneficial. Candidates should also have a keen interest in high-impact research work at the interface of engineering and medicine. For other criteria, please refer to the FAQ at

How to Apply:
Applicants should have an undergraduate degree of at least 2(i) standard (or equivalent) in a quantitative discipline such as engineering, physics, computer science or mathematics. For informal enquiries please contact [Email Address Removed]. Applications should be submitted through the University of Sheffield admissions system

Funding Notes

Students will need to arrange their own funding, and a list of potential funding sources can be found on the departmental web pages

Related Subjects

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