PhD Studentship - Using artificial intelligence to enhance screening for atrial fibrillation
This is a 3.5-year studentship in the application of Engineering in Medicine funded by the W.D Armstrong Trust Fund at the University of Cambridge.
Background: Atrial fibrillation (AF) is a heart arrhythmia which is associated with a fivefold increase in risk of stroke, and yet is undiagnosed in 425,000 people in England. We are investigating whether screening for AF could reduce the incidence of stroke in the SAFER Trial. During screening, patients use a handheld device to record their electrocardiogram (ECG) four times a day for three weeks. This results in a large number of ECG recordings which must be manually reviewed by clinicians to diagnose AF. This process is costly and time-consuming.
Project: The aim of this project is to develop a clinical decision support tool to aid ECG interpretation for use in AF screening studies. The objectives are: (1) To develop a model to prioritise ECGs for review using machine learning and signal processing; (2) To develop visualisation techniques to aid ECG interpretation using explainable artificial intelligence; and (3) To assess the acceptability and performance of the resulting clinical decision support tool in collaboration with cardiologists.
Research Environment: The PhD studentship will be hosted at the Department of Public Health and Primary Care (https://www.phpc.cam.ac.uk/). This department is running the SAFER Trial, and has a labelled dataset of 100,000s of ECGs recorded in this real-world AF screening programme. The PhD will be co-supervised by Dr Peter Charlton and Prof Jonathan Mant from this department, and Dr Elena Punskaya from the Department of Engineering. The student will be encouraged to integrate into the SAFER Research Team, and to work with our academic, clinical and industrial partners to ensure their work could have real-world impact.
Value to the Student: The student will develop valuable skills in both engineering and clinical domains: machine learning, explainable artificial intelligence, signal processing, and the analysis of clinical trial data. The multi-disciplinary supervision provided will equip the student for a future career at the interface of engineering and medicine.
Requirements: Applicants should have (or expect to obtain by the start date) at least a good 2:1 (and preferably a Master's degree) in engineering or a related subject such as computer science, mathematics, statistics or physics.
Benefits: We invite applications from UK and non-UK students. The studentship provides a stipend of £16,062 per year for 3.5 years. UK-level tuition fees are covered: other applicants will need to secure additional funding for overseas student fees. Please ensure you meet the University of Cambridge entrance requirements: see https://www.postgraduate.study.cam.ac.uk/application-process/entry-requirements.
Enquiries: We welcome informal discussions about this post. Please contact Dr Peter Charlton: firstname.lastname@example.org
How to apply
To apply please click https://www.postgraduate.study.cam.ac.uk/courses/directory/cvphpdhpc
Course details: PhD in Public Health & Primary Care (Full-time)
Start Date: 1st October, 2022 (Michaelmas Term 2022)
Supervisors: Dr Peter Charlton, Dr Elena Punskaya, and Professor Jonathan Mant
With your application please upload:
I. A letter (2,500 character limit) outlining your suitability, why you are interested in a PhD in this area, your background and research interests
II. Your CV
III. Your academic transcripts and
IV. Details of two academic referees.
Interview and Selection process
The deadline for application is 6th June 2022
Applicants will be notified of the outcome of their application by 20th June 2022
Shortlisted candidates will be invited to interview in the week of 27th June 2022
Applicants will be notified of the outcome of their interview soon after.
Please quote reference SN31180 on your application and in any correspondence about this vacancy.
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