Research Associate in digital twin modelling for thrombosis risk predication

Job id: 160605 . Salary: £45,031 - £47,379 per annum inclusive of London Weighting Allowance .

Posted: 14 October 2026 . Closing date: 01 November 2026 .

Business unit: Faculty of Life Sciences & Medicine . Department: Res Dept of Digital Twins for Healthcare .

Contact details: Adelaide de Vecchi . Adelaide.de_vecchi@kcl.ac.uk

Location: St Thomas' Hospital . Category: Research .

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About Us

King’s College London is an internationally renowned university delivering exceptional education and world-leading research. The school of Biomedical Engineering & Imaging Sciences (BMEIS) at King’s College London is a world-leading centre for transformative research at the intersection of engineering, medicine, and data science, which brings together cutting-edge technology, clinical expertise, and interdisciplinary innovation.

We are recruiting a Postdoctoral Research Associate (PDRA) to work on a 3-year EPSRC-funded project on developing a probabilistic digital twin framework for efficient, dynamic prediction of thrombogenesis risks in cardiovascular diseases.

The role will be based within the Research Department of Digital Twins in Healthcare, part of the School of Biomedical Engineering & Imaging Sciences, King's College London. The Department is located within St Thomas' Hospital, one of the largest medtech research facilities embedded within an NHS hospital. They will work in an established, diverse group bringing together expertise in cardiovascular digital twins, artificial intelligence, computational modelling and clinical research .

About

The Role

The post holder will join a team of researchers to develop innovative and scalable cardiovascular digital twins for personalised cardiovascular care. The work will focus on novel strategies to integrate multi-modal patient datasets (including EHRs, imaging, lab tests) with population-level prior information, enabling a scalable digital twin to predict risks of developing thrombosis, with the long term vision of supporting a precision approach to anticoagulation therapy.

Main responsibilities

• Develop a digital‑twin framework for modelling mechanisms of blood coagulation in two different type of patients (atrial fibrillation and coronary heart disease)

• Develop and benchmark probabilistic kernel-based and deep-learning surrogates to rapidly infer time-dependent thrombosis biomarkers from imaging, haemodynamic and blood data, with quantified uncertainty and robust generalisation to unseen anatomies.

• Integrate the digital twin with probabilistic Bayesian frameworks and derive population‑level prior models from representative cohorts and primary care data.

This is a full-time post (35 hours per week), and you will be offered a fixed term contract until 18th October 2030.

Research staff at King’s are entitled to at least 10 days per year (pro-rata) for professional development. This entitlement, from the Concordat to Support the Career Development of Researchers, applies to Postdocs, Research Assistants, Research and Teaching Technicians, Teaching Fellows and AEP equivalent up to and including grade 7. Visit the Centre for Research Staff Development for more information.

About

You

To be successful in this role, we are looking for candidates to have the following skills and experience:

Essential criteria

• PhD in biomedical engineering, computer science, applied mathematics, computational science, physics or a related discipline.

• Strong scientific programming skills, preferably in Python and/or C++.

• Demonstrable expertise in at least two of the following: biophysical computational modelling using commercial software (ANSYS), Gaussian-process emulators, deep learning models, neural and hybrid physics-informed models.

• Experience with medical imaging data processing (e.g. segmentation).

• Ability to undertake independent quantitative research and work effectively within a multidisciplinary team.

Desirable criteria

• Experience with multi-modal patient data curation and preparation.

• Experience with healthcare foundation models.

• Understanding of Bayesian inference and Bayesian personalisation methods.

Downloading a copy of our Job Description

Full details of the role and the skills, knowledge and experience required can be found in the Job Description document, provided at the bottom of the page. This document will provide information of what criteria will be assessed at each stage of the recruitment process.

Please note that this is a PhD level role but candidates who have submitted their thesis and are awaiting award of their PhDs will be considered. In these circumstances the appointment will be made at Grade 5, spine point 30 with the title of Research Assistant. Upon confirmation of the award of the PhD, the job title will become Research Associate and the salary will increase to Grade 6.

Further Information

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.

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.

We are committed to free speech and to academic freedom, believing that our foundational purpose as a university, is to create spaces where a wide range of ideas, including ideas that are controversial, can be discussed and debated, and where members of our community can express lawful views without fear of intimidation, harassment or discrimination.

When engaging in the robust exchange of ideas, we ask that our community is mindful of our Dignity at King’s guidance.

We ask all candidates to submit a copy of their CV, and a supporting statement, detailing how they meet the essential criteria listed in the person specification section of the job description. If we receive a strong field of candidates, we may use the desirable criteria to choose our final shortlist, so please include your evidence against these where possible.

To find out how our managers will review your application, please take a look at our ‘How we Recruit’ pages.

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