Supervisors

Academic Supervisors:
Dr Yanda Meng, University of Exeter
Professor Mark Kelson, University of Exeter

External Supervisors:
Professor Yalin Zheng, Liverpool Centre for Cardiovascular Science
Professor Gregory Lip, Liverpool Centre for Cardiovascular Science
Professor Dhiraj Gupta, Liverpool Centre for Cardiovascular Science

Location:

Department of Computer Science, Streatham Campus, Exeter
and
Liverpool Centre for Cardiovascular Science, William Henry Duncan Building, Liverpool


The University of Exeter’s Department of Computer Science is inviting applications for a PhD studentship funded by Liverpool Centre for Cardiovascular Science (LCCS) and the Faculty of Environment, Science and Economy to commence on January, 2025 or as soon as possible thereafter.  For eligible students the studentship will cover home or international tuition fees plus an annual tax-free stipend of at least £19,237 for 3 years full-time study. The student would be based in the Department of Computer Science in the Faculty of Environment, Science and Economy at the Streatham Campus in Exeter and expected to spend majority time (approximately 9 months per year) in Liverpool Centre for Cardiovascular Science, William Henry Duncan Building, University of Liverpool.

Project Description:

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with significant morbidity and mortality [1]. However, its clinical presentation and response to treatment vary widely among individuals, highlighting the need for a deeper understanding of AF heterogeneity [2]. This interdisciplinary project aims to elucidate the complex landscape of AF through the integration of advanced statistics and machine learning techniques. Leveraging electrophysiology (EP) clinical data from the Liverpool Centre for Cardiovascular Science, Liverpool Heart and Chest Hospital NHS Foundation Trust and "big data" sources like TriNetX and UK Biobank, the study will apply innovative analytical frameworks to unravel distinct AF subtypes and identify underlying disease mechanisms. By comprehensively characterizing AF phenotypes and patient subgroups, the research seeks to inform personalized approaches to AF management and treatment, ultimately improving patient outcomes and advancing precision medicine in cardiology.  Extensive evaluation will be conducted utilising a local large-scale retrospective dataset encompassing EP recording and corresponding clinical indices sourced from the Liverpool Centre for Cardiovascular Science. This multi-centre dataset will be a robust benchmark for assessing the model's performance and resilience across diverse clinical contexts.
This project is in partnership with Liverpool Centre for Cardiovascular Science (LCCS). The successful PhD candidate will benefit from working with a multidisciplinary team in which there exists extensive experience in the areas of machine learning, biostatistics, and medicine: Dr Yanda Meng (Machine Learning), Prof Mark Kelson (Biostatistics), Prof Yalin Zheng (AI in Healthcare), Prof Dhiraj Gupta (Cardiovascular Medicine),  Prof Gregory Lip (Cardiovascular Medicine).

If you have any specific questions regarding this studentship, please contact Dr Yanda Meng at y.m.meng@exeter.ac.uk
The studentship will be awarded on the basis of merit for 3 years of full-time study to commence on 23 January 2025.

The collaboration involves a project partner who is providing funding and other material support to the project, this means there are special terms that apply to the project, these will be discussed with Candidates at Interview and fully set out in the offer letter. Full details will be confirmed at offer stage.
International applicants need to be aware that you will have to cover the cost of your student visa, healthcare surcharge and other costs of moving to the UK to do a PhD.

Entry requirements

• Applicants for this studentship must have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK, in an appropriate area of science or technology. 
• Possess a strong academic background, ideally with a bachelor's degree in computer science, statistics, physics, engineering, or a related field.
• Have practical experience working on AI for healthcare projects, utilizing PyTorch and/or TensorFlow libraries.
• Exhibit proficiency in programming languages such as Python, C++, C, or Java.
• Preference will be given to candidates with prior experience in presenting or preparing scientific manuscripts for publication in journals or conferences.
• If English is not your first language you will need to meet the required level as per our guidance at https://www.exeter.ac.uk/pg-research/apply/english/

How to apply

Apply now

In the application process you will be asked to upload several documents. 
• CV
• Letter of application (outlining your academic interests, prior research experience and reasons for wishing to undertake the project).
• Transcript(s) giving full details of subjects studied and grades/marks obtained (this should be an interim transcript if you are still studying)
• Names of two referees familiar with your academic work. 
• If you are not a national of a majority English-speaking country you will need to submit evidence of your proficiency in English.
The closing date for applications is midnight on 25th August 2024.  Interviews will be held virtually on the MS Teams/Zoom in the week commencing 26th August.
If you have any general enquiries about the application process please email PGRApplicants@exeter.ac.uk or phone 0300 555 60 60 (UK callers) +44 (0) 1392 723044 (EU/International callers)  Project-specific queries should be directed to the main supervisor.

Summary