Research Associate (767821)

FTE: 1 (35 hours/week)

Contract type: Fixed Term (18 months)

You will develop patient-specific vascular models from medical images to drive AI and CFD approaches for next-generation surgical planning and intervention. This post sits within a large, multidisciplinary EPSRC-funded Transformative Healthcare Technologies project “Real-time Digital Twin Assisted Surgery” (Ref: EP/X033686/1, £4M), bringing together expertise in medical imaging, computational modelling, artificial intelligence, surgery and biomedical engineering.

In this role, you will lead the image-to-model framework, transforming clinical imaging data into high-fidelity 3D vascular geometries that underpin both data-driven (AI) and physics-based (CFD) digital twins. Your work will shape how personalised surgical strategies are simulated, optimised and ultimately translated into the operating theatre. You will work closely with clinicians, AI researchers, computational modellers and biofabrication experts, and will be encouraged to lead methodological development, publish in high-impact journals, and contribute to the next generation of digital-twin-enabled surgery.

As a Research Associate, under the general guidance of a research leader, you will develop research objectives and proposals, play a lead role in relation to a specific project/s or part of a broader project, conduct individual and/or collaborative research, contribute to the development of new research methods, identify sources of funding, and contribute to the securing of funds for research, including drafting grant proposals and planning for future proposals. You will write up research work for publication, individually or in collaboration with colleagues, and disseminate the results via peer reviewed journal publications and presentation at conferences. You will join external networks to share information and ideas, inform the development of research objectives and to identify potential sources of funding. You will collaborate with colleagues to ensure that research advances inform departmental teaching effort and you will collaborate with colleagues on the development of knowledge exchange activities by, for example, participating in initiatives which establish research links with industry and influence public policy and the professions. You will supervise student projects, provide advice to students and contribute to teaching as required by, for example, running tutorials and supervising practical work. You will contribute in a developing capacity to the Department, Faculty and/or University administrative and management functions and committees and engage in continuous professional development.

To be considered for the role, you will be educated to a minimum of PhD level in an appropriate discipline, or have significant relevant experience in addition to a relevant degree. You will have sufficient breadth or depth of knowledge in vascular image segmentation, 3D vessel reconstruction, computational fluid dynamics (CFD), computer-aided design (CAD), morphing algorithms, fluid-structure interaction, Windkessel boundary conditions, 0D/1D-3D coupling, and a developing ability to conduct individual research work, to disseminate results and to prepare research proposals. You will have an ability to plan and organise your own workload effectively and an ability to work within a team environment. You will have excellent interpersonal and communication skills, with the ability to listen, engage and persuade, and to present complex information in an accessible way to a range of audiences.

Informal enquiries about the post can be directed to Asimina Kazakidi, Reader, (asimina.kazakidi@strath.ac.uk).

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