Multimodal Modelling of Personalised Haemodynamics in Heart Failure with Preserved Ejection Fraction (HFpEF)
About the Project
Supervisory Team: Dr Caglar Ozturk, Dr Mihaela Paun and Prof Nick Curzen
Heart failure affects millions, yet personalised treatments remain limited. This project develops advanced computational models to simulate the progression of patient-specific heart function in heart failure with preserved ejection fraction (HFpEF). It combines image-based fluid–structure interaction modelling and statistical emulation to improve diagnosis and enable more precise treatments.
You'll work with clinical imaging data, such as magnetic resonance imaging (MRI) and computed tomography (CT), to build detailed patient-specific models that capture both heart mechanics and blood flow through fluid–structure interaction approaches. These models will be used to estimate important physiological properties, including myocardial stiffness and filling pressures, in a non-invasive way.
A key part of the project will involve creating virtual patient cohorts to represent different HFpEF phenotypes and using these to investigate how patients may respond to various treatments through in-silico studies. To make these simulations more efficient, you'll explore the use of statistical emulators and machine learning methods, enabling faster predictions and supporting inverse modelling approaches for personalised parameter estimation.
The project offers training at the intersection of computational modelling, data science, and cardiovascular engineering, with access to state-of-the-art simulation tools and experimental platforms. Training will cover feature extraction, phenotype-specific analysis, and interdisciplinary collaboration between engineering and medicine.
Entry requirements
You must have a UK 2:1 honours degree or its international equivalent.
Fees and funding
Tuition fees will be paid, and you'll receive an annual stipend.
This project is co-funded by Wessex Medical Research.
How to apply
You need to:
- choose programme type (Research), 2026/27, Faculty of Engineering and Physical Sciences
- select Full time or Part time
- search for programme PhD Engineering & the Environment (7175)
- add name of the supervisor in section 2 of the application
Applications should include:
- your CV (resumé)
- 2 academic references
- degree transcripts and certificates to date
- English language qualification (if applicable)
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