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Analysing ECG with deep learning: human supervised analysis versus A.I. (Artificial Intelligence)

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

Project Description

This is a unique and exciting opportunity to learn new skills in artificial intelligence and ageing biology. You will combine patch-clamp electrophysiology with cutting edge machine learning to predict and understand how cells in the heart age. You will develop fundamental biological models of ion channel activity and test them by recording single-molecule activity, in real-time in live cells. This timely studentship will provide training in a unique set of highly sought-after research skills in the very latest multi-million pound world-class research facility. You will have access to the latest in High Performance Computing clusters.

You will have an interest in combining programming and biological research to understand cardiac physiology. The ideal candidate would have some experience of scripting/coding or an interest in learning these skills, although full training of all techniques will be provided in a supportive environment. We would be interested in applications from biologists, mathematicians or computer scientists keen to apply their skills to ageing biology.

The student will be a member of the Department of Musculoskeletal Biology Group which is focused on the comparative biology of the musculoskeletal system of humans and animals. It is a dynamic environment where the student will be exposed to a broad range of expertise from scientific researchers and veterinary and human clinicians. The department is part of the Institute of Ageing and Chronic Disease which has recently been awarded Athena Swan Silver status in recognition of its commitment to providing gender equality and a flexible working environment. We offer a supportive working environment with flexible family support for all our staff and students and applications for part-time study are encouraged.

Funding Notes

Note the successful applicant must have full funding available for living costs, student fees and project running costs.

To apply, please email your CV with a covering letter to Dr Barrett-Jolley via with a copy to

How good is research at University of Liverpool in Clinical Medicine?
(joint submission with Liverpool School of Tropical Medicine)

FTE Category A staff submitted: 143.50

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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