CDTnet PhD Fellowship F7: Models Non-Invasive Estimation of Blood Pressure and Flow Inefficiencies

School of Biomedical Engineering & Imaging Sciences

Organisation/Company King's College London Department School of Biomedical Engineering & Imaging Sciences Research Field Engineering » Biomedical engineering Computer science » Modelling tools Researcher Profile First Stage Researcher (R1) Positions PhD Positions Final date to receive applications 30 Sep 2026 - 23:59 (Europe/Brussels) Country United Kingdom Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme?

Horizon Europe - MSCA Marie Curie Grant Agreement Number  Is the Job related to staff position within a Research Infrastructure? No

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 King’s 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.

• 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.

• Degree in engineering, computer science, physics, mathematics, statistics or related quantitative discipline (UK 2:1 or equivalent).
• Working understanding of modelling, machine learning techniques and medical imaging principles. No specific medical knowledge is required.
• Bonus: experience coding in Python or equivalent programming language.

• MSCA Mobility Rule:
  You must not have lived or worked in the United Kingdom for more than 12 months in the 3 years before recruitment.
• MSCA Eligibility Rule:
  You must not already hold a doctoral degree and must be eligible to enrol in the PhD programme at King's College London.