Research Associate: Digital Twinning and In Silico Trials

• Generous employer contribution pension
• 29 days annual leave plus bank holidays, along with Christmas closure
• Ride to work and EV car scheme available

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We are seeking a motivated and collaborative individual to join our team as a Research Associate: Digital Twinning and In Silico Trials. This role offers an exciting opportunity to contribute to the University of Manchester's contribution to Federated Data Science UK, a national consortium building a federated network of secure health-data environments. You will lead the scientific delivery of its flagship use case, "Accelerating Innovation - Transforming Medical Device Development Through In Silico Trials", scaling in silico trials of cardiovascular and cerebrovascular medical devices to very large virtual-patient populations built from real-world imaging and electronic health record data. The opportunity is to help define the specifications and evaluation standards for running in silico device trials on national data infrastructure, accelerating the development, approval and adoption of safer medical devices.

• Leading the scientific delivery of the in silico trials use case, scaling the existing FD-PASS intracranial aneurysm device trial cohort more than a hundred-fold using digital twins built from federated imaging and electronic health record data, and informing device selection through head-to-head comparative in silico trials.
• Developing automated, efficient and reproducible workflows for image-based modelling and simulation of the cardiovascular and cerebrovascular systems, with and without implanted devices, suitable for deployment across federated secure data environments.
• Defining the use cases, technical specifications and data and compute requirements for in silico device trials and translating these into concrete requirements for national federated research infrastructure, including its interoperability and provenance tooling.
• Designing and delivering evaluation and benchmarking frameworks, including model credibility (verification, validation and uncertainty quantification), to assess device safety, efficacy and failure modes across virtual populations.
• Developing generative and statistical anatomical models, deep-learning models of flow fields, and person-specific physiological boundary conditions derived from real-world clinical and socioeconomic data, to build virtual populations for structural heart, aortic, carotid and cerebrovascular applications.

We welcome candidates who bring diverse perspectives, experiences, and approaches to their work.

We encourage applications from individuals with a wide range of backgrounds and experiences. You should demonstrate:

We value transferable skills and real-world experience as much as formal qualifications.

Name: Professor Alejandro Frangi
Email Address: alejandro.frangi@manchester.ac.uk

recruitmentservices.people@manchester.ac.uk

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Applications close at midnight on the closing date.

Further particulars (with person specification) linked below.