PhD Fellowship: Scalable Digital Twin Cardiovascular Modeling

King's College London; Greater London; On-site; GBP 18,000 - 21,000; Full time

Benefits offered by this job

Maastricht University secondment

University of Zagreb secondment

University of Zaragoza secondment

GE Vingmed Ultrasound secondment

Job summary

King's College London’s School of Biomedical Engineering & Imaging Sciences invites applications for a doctoral candidate to develop methods for personalising cardiovascular digital twins at scale.

You will work with large imaging datasets, estimate anatomical, mechanical and electrophysiological parameters, and explore model complexity versus clinical usefulness, including population-scale datasets like UK Biobank.

Qualifications

• Experience with computationally intensive analysis required.
• Experience in image or bio-signal analysis desirable.
• Experience with model personalization: parameter estimation, uncertainty quantification, identifiability.

Responsibilities

• Develop methods to personalise cardiovascular digital twins at scale.
• Work with large imaging datasets and clinical data workflows.
• Balance model complexity with clinical applicability for robust twins.
• Contribute to population-scale digital twin frameworks across cardiac conditions.

Skills

Computational analysis

Image analysis

Bio-signal analysis

Model personalization

Tools

CircAdapt platform

Clinical data workflows

Job description

King's College London’s School of Biomedical Engineering & Imaging Sciences invites applications for a doctoral candidate to develop methods for personalising cardiovascular digital twins at scale.

You will work with large imaging datasets, estimate anatomical, mechanical and electrophysiological parameters, and explore model complexity versus clinical usefulness, including population-scale datasets like UK Biobank.