Scholarships Merit-based
King's College London

AI Evaluation of Heart Muscle Function Studentship

View relevant PhDs
Multiple disciplines
20622 GBP
United Kingdom
02 Oct 2023
Application deadline


King’s College London is an internationally renowned university delivering exceptional education and world-leading research. They are dedicated to driving positive and sustainable change in society and realising our vision of making the world a better place. 


Scholarship type

Merit based

Number of scholarships to award



Full tuition waiver + £20,622

Scholarship coverage

  • tuition fee reduction
  • living expenses


Cardiovascular disease is the most common cause of death worldwide. Detection of heart damage early is important to identify which patients are at risk. Imaging such as with Cardiac MRI is potentially well suited to detect damage at an early stage, and may allow preventative therapy to be started.

‘Strain’ is a potentially important biomarker of early damage. Strain is the amount of shortening and lengthening of heart muscle in each region of the heart. This project will develop novel artificial intelligence (AI) methods to automatically extract strain from standard MRI clinical imaging scans. The aims are: 1) develop tools for robust quantification of local strain; 2) train AI methods to predict accurate strain from standard MRI exams, and 3) use strain to predict outcomes in patients with heart disease.

They anticipate that this project will lead to implementation of AI driven solutions to enhance patient diagnosis and treatment. In particular, this project will enable better management of patients undergoing chemotherapy for cancer, who need constant monitoring to see if heart function is impaired due to cancer therapy and for detection of early-stage heart failure.

This is a collaborative project between the Biomedical Engineering and Imaging Sciences department of King’s College London and the heart failure and cardio-oncology unit at Royal Brompton Hospital.

Applicable programmes


The studentship is fully funded for 3.5 years. This includes home tuition fees, stipend and generous project consumables.

Stipend: Students will receive a tax-free stipend at the UKRI rate of £20,622 (AY 2023/24) per year as a living allowance.

Research Training Support Grant (RTSG): A generous project allowance will be provided for research consumables and for attending UK and international conferences.


  • Candidates who meet the eligibility requirements for Home Fee status will be eligible to apply for this project. Home students will be eligible for a full UKRI award, including fees and stipend, if they satisfy the UKRI criteria below, including residency requirements.

 To be classed as a Home student, candidates must meet the following criteria:

  • be a UK National (meeting residency requirements), or
  • have settled status, or
  • have pre-settled status (meeting residency requirements), or
  • have indefinite leave to remain or enter.
  • Prospective candidates should have a 1st or 2:1 M-level qualification in Biomedical Engineering, Physics, Engineering, Computer Science, Mathematics, or a related programme.
  • Preference will be given to candidates with a background conducive to multidisciplinary research and preferably programming skills.
  • They welcome eligible applicants from any personal background, who are pleased to join diverse and friendly research groups.

Scholarship requirements


Multiple disciplines


United Kingdom



Study experience required





Application deadline

02 Oct 2023

Please submit an application for the Biomedical Engineering and Imaging Science Research MPhil/PhD (Full-time) programme using the King’s Apply system. Please include the following with your application:

A PDF copy of your CV should be uploaded to the Employment History section.

A 500-word personal statement outlining your motivation for undertaking postgraduate research should be uploaded to the Supporting statement section.

Funding information: Please choose Option 5 “I am applying for a funding award or scholarship administered by King’s College London” and under “Award Scheme Code or Name” enter BMEIS_SN_AY. Failing to include this code might result in you not being considered for this funding.

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