CDTnet PhD Fellowship F7: Models for the Non-Invasive Estimation of Central Blood Pressure and Flow Inefficiencies King's College London in United Kingdom

King’s College London is offering a funded PhD Fellowship within CDTnet on models for the non-invasive estimation of central blood pressure and flow inefficiencies. The project sits in the School of Biomedical Engineering & Imaging Sciences and brings together biomedical engineering, computer science, medical imaging, machine learning, and computational modelling to tackle an important cardiovascular challenge.

Many valve and congenital cardiovascular conditions are currently assessed using invasive catheter-based pressure measurements. This project aims to develop safer, non-invasive alternatives by estimating pressure differences directly from MRI and ultrasound data. The doctoral researcher will investigate how blood flow loses energy through narrowed valves and abnormal vascular structures, with the goal of quantifying pressure drops, viscous energy losses, and momentum loss as clinically useful biomarkers of valve and vascular function.

The research builds on technologies developed at King’s College London and uses imaging datasets from St Thomas’ Hospital, including cases involving aortic valve stenosis, congenital cardiovascular disorders, and cardiovascular studies related to preterm birth. The candidate will work on advanced image analysis, fluid dynamics, and machine learning methods, and will also explore ultrasound-based approaches such as planar wave imaging and contrast-enhanced ultrasonography. The long-term aim is to support future clinical implementation and more personalised assessment of cardiovascular disease.

The fellowship includes planned secondments with FEops (Belgium), Maastricht University (Netherlands), and IDIBAPS (Spain), offering exposure to commercial computational modelling, experimental validation, and clinical/in-silico data validation.

Applicants should have a Bachelor degree or equivalent, ideally a UK 2:1 or equivalent in engineering, computer science, physics, mathematics, statistics, or a related quantitative discipline. A working understanding of modelling, machine learning, and medical imaging is desirable; coding experience in Python or a similar language is a plus. No specific medical background is required. The post is funded by Horizon Europe MSCA under grant agreement 101312147.

Eligibility is subject to the MSCA Mobility Rule: applicants must not have lived or worked in the United Kingdom for more than 12 months in the 3 years before recruitment. Applicants must also not already hold a doctoral degree and must be eligible to enrol in the PhD programme at King’s College London. Excellent English is required.

The application deadline is 30 September 2026. Applications should be submitted through the CDTnet website.