Research Assistant and PhD Fellowship in Cardiovascular Digital Twins (F7) - Strand, London, WC2R 2LS

Research Assistant and PhD Fellowship in Cardiovascular Digital Twins (F7) - Strand, London, WC2R 2LS

About Us

A 3-year PhD-fellowship is available at the School of Biomedical Engineering & Imaging Sciences at King's College London. This position is part of the Cardiovascular Digital Twin network consisting of 15 fellowships across Europe ( CDTnet ), funded by the European Research Councils MSCA programme.

As a world-leading, research-intensive university ranked among the global top 40, King's offers PhD candidates an exceptional academic environment co-located with clinical teams at St Thomas' Hospital, uniquely bridging the gap between research, medical imaging, and active patient care. Within this framework, our Digital Twins for Healthcare department pioneers precision medicine by engineering virtual patient replicas to predict and treat complex diseases.

About TheRole

Many cardiovascular conditions, including valve disease and congenital heart disorders, are assessed using pressure measurements obtained through invasive catheter procedures. While these measurements provide valuable clinical information, they are costly, time-consuming and carry risks for patients. Developing reliable non-invasive alternatives could significantly improve diagnosis and patient care.

This project aims to develop new methods for estimating blood pressure and quantifying flow inefficiencies using medical imaging, machine learning, and computational modelling. The doctoral candidate will investigate how pressure differences can be calculated directly from magnetic resonance imaging (MRI) and ultrasound data, reducing the need for invasive measurements in selected clinical applications.

Building on technologies developed at Kings College London, the project will focus on understanding how blood flow loses energy as it passes through narrowed valves or abnormal vascular structures. The candidate will develop methods to characterise pressure drops, viscous energy losses and momentum loss within the cardiovascular system, providing new biomarkers of valve and vascular function. The project will also explore practical ultrasound-based approaches, including planar wave imaging and contrast-enhanced ultrasonography, with the goal of supporting future clinical implementation.

The research will draw on extensive imaging datasets from St Thomas Hospital, including patients with aortic valve stenosis, congenital cardiovascular disorders and individuals participating in studies investigating the cardiovascular consequences of preterm birth. By combining fluid dynamics, machine learning, and advanced imaging analysis, the project seeks to develop clinically useful tools that support safer, more accessible and more personalised assessment of cardiovascular disease.

Planned Secondments

FEops, Belgium (1 month): exposure to the commercial application of computational modelling technologies for planning transcatheter aortic valve implantation (TAVI) procedures.

Maastricht University, Netherlands (2 months): experimental validation of flow inefficiency measurements and collaboration with related valve disease projects.

IDIBAPS, Spain (2 months): access to clinical datasets and validation of developed algorithms against clinical and in-silico data.