AI-enhanced high-fidelity cardiovascular modelling

Are you interested in exploring cardiovascular biomechanics and machine learning in biomedical applications? Be part of a research that merges AI-based medical image processing and multiphysics cardiovascular modelling towards a better understanding of heart diseases and developing patient-specific treatment options.

In this project, you will develop high-fidelity, high-resolution, multi-scale models of the human heart and vasculature with the latest breakthroughs in AI/machine learning. These high-fidelity models will be used to enhance personalized treatment planning and post-treatment monitoring for patients suffering from hemodynamically complicated structural heart diseases, including congenital/paediatric heart diseases.

The research will be conducted in the Bioengineering research group, in the Department of Mechanical Engineering at the University of Southampton under the supervision of Dr Caglar Ozturk. Dr Ozturk’s research focuses on the experimental, computational, and clinical aspects of cardiovascular biomechanics in order to understand, investigate, and predict the multiscale behaviour of the human heart and circulatory systems.

https://www.southampton.ac.uk/people/65cfns/doctor-caglar-ozturk

The student will work in an interdisciplinary, global, collaborative, and supportive environment. Collaborate closely with clinicians to gather clinical data and guidance, as well as to illustrate the benefits of the developed framework in clinical settings. You will actively participate in weekly lab meetings, write scientific articles and reports, and give presentations and workshops at national and international conferences. 

The student will be trained to gain skills in: 

• Advanced cardiac modelling 
• Soft tissue biomechanics
• Clinical image/signal processing
• Fluid-structure interaction modelling 
• Machine learning algorithms

Entry requirements

A UK 2:1 honours degree, or its international equivalent.

Affinity with scientific machine learning, statistical shape modelling, data-driven modelling, and/or numerical analysis of PDEs and ODEs on complex domains is desirable.

How to apply

https://student-selfservice.soton.ac.uk/BNNRPROD/bzsksrch.P_Search

You need to:

• choose programme type (Research), 2025/26, Faculty of Engineering and Physical Sciences
• select Full time or Part time
• choose the relevant PhD in Eng & Env
• add name of the supervisor in section 2

Applications should include:

• personal statement
• your CV (resumé)
• 2 academic references
• degree transcripts to date