Research Assistant and PhD Fellowship in Cardiovascular Digital Twins (F5)

Cardiovascular digital twins have the potential to transform medicine by providing personalised models of anatomy and physiology that support diagnosis, prognosis and treatment planning. However, creating these models currently requires complex and time-consuming workflows that limit their use in large patient populations and routine clinical practice. This project aims to develop new methods for personalising cardiovascular digital twins at scale. The doctoral candidate will create computational approaches capable of estimating anatomical, mechanical and electrophysiological parameters from large imaging datasets while addressing the challenges of limited patient-specific data and uncertainty in model predictions. A key objective is to identify the appropriate balance between model complexity and clinical applicability, allowing robust digital twins to be generated efficiently for large numbers of individuals. The project will make extensive use of the UK Biobank, one of the world's largest health research resources, with access to imaging and functional data from about 100,000 participants. Using these data, the candidate will develop population-based reference values for cardiac anatomy and function, including parameters such as contractility, passive stiffness and electrical conduction properties. The project will also investigate how these parameters change over time by studying longitudinal data from thousands of participants and constructing digital twin trajectories that describe cardiovascular ageing and disease progression. An important aspect of the research will be the study of variability across populations, including differences related to sex and ethnicity. The resulting methodologies and datasets will support many of the other CDTnet projects by providing population-scale digital twin frameworks that can be applied to heart failure, arrhythmias and valve disease. Ultimately, the project aims to help make personalised cardiovascular modelling more accessible, scalable and clinically useful. Planned Secondments

• Maastricht University, Netherlands (2 months): training in the CircAdapt cardiovascular modelling platform and large-scale model personalisation.
• University of Zagreb School of Medicine and IDIBAPS, Spain (short stays): exposure to clinical data and workflows related to heart failure, arrhythmias and valve disease.
• University of Zaragoza, Spain (short stay): integration of clinical datasets into digital twin pipelines and support for related CDTnet projects.
• GE Vingmed Ultrasound, Norway (2 months): investigation of digital twin personalisation from echocardiographic data., The Equality Act of 2010 protects the rights of our students and staff and provides a framework to fulfil our duties to eliminate unlawful discrimination, harassment and victimisation and in addition, to advance equality of opportunity and foster good relations between those who share a protected characteristic and those who do not. At times, this will include balancing rights and beliefs that can feel in tension.
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Essential criteria
• A 1st or 2:1 Honours degree (or international equivalent) in a highly quantitative discipline like Biomedical Engineering, Computer Science, Mathematics, or Physics
• Meet all mandatory MSCA mobility rules - applicants must not have lived or worked in the UK for over 12 months in the 36 months prior to recruitment
• Experience in programming and computational modelling using languages like Python, C++, or MATLAB
• Strong analytical and organisational skills to apply initiative, manage timelines, and work independently on research challenges
• Meet King's College London's postgraduate English language proficiency requirements for scientific writing and presentations
• Willingness to work within an international network and to support a positive research culture
Desirable Project-Specific Qualifications and Skills
• Evidence of ability to work with computationally intensive analysis routines
• Evidence of skills in image or bio-signal (e.g. ECG) analysis
• Evidence of skills in model personalisation: parameter estimation, uncertainty quantification, model identifiability
  
  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 ( ), funded by the European Research Council's 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., At King's, we believe that the diversity of our community and a culture that is welcoming, open, inclusive and collaborative, are great strengths of the university.

Grade and Salary : £45,691.50 per year (including mobility allowance), plus family allowance if eligible Job ID : 159025