Pablo Lamata

CDTnet PhD Fellowship F5 at King's College London offers a funded doctoral opportunity in cardiovascular digital twins , focused on developing methods to infer anatomical, mechanical, and electrophysiological parameters at scale. The project sits in the School of Biomedical Engineering & Imaging Sciences and combines biomedical engineering , computational modelling , and data-driven analysis. The successful candidate will work on personalising cardiovascular digital twin models using large imaging datasets and uncertainty-aware model estimation. A major resource for the project is the UK Biobank , providing access to imaging and functional data from around 100,000 participants, along with longitudinal data to study cardiovascular ageing and disease progression. Research themes include building population-based reference values for cardiac anatomy and function, estimating parameters such as contractility, passive stiffness, and electrical conduction properties, and exploring variation across populations, including sex- and ethnicity-related differences. The methods developed are intended to support broader CDTnet research in heart failure, arrhythmias, and valve disease, with the goal of making personalised cardiovascular modelling more scalable and clinically useful. The fellowship includes planned secondments and training opportunities with Maastricht University (CircAdapt modelling and large-scale personalisation), University of Zagreb School of Medicine and IDIBAPS (clinical workflows and cardiovascular disease data), University of Zaragoza (integration of clinical datasets), and GE Vingmed Ultrasound in Norway (echocardiographic digital twin personalisation). Funding: The position is funded through Horizon Europe / MSCA Marie Curie under grant agreement 101312147 . Eligibility: Applicants must have at least a bachelor degree or equivalent, be eligible for PhD enrolment at King's College London, not already hold a doctoral degree, and comply with the MSCA mobility rule. Strong English is required. Desirable experience includes computationally intensive analysis, image or bio-signal analysis, and model personalisation techniques such as parameter estimation and uncertainty quantification. Deadline: 30 September 2026. Apply: Submit your application through the CDTnet website at https://www.cdtnet.eu/apply-now.