Dame Kathleen Ollerenshaw Fellow Cardiovascular AI & Scientific Computing

BACKGROUND

The Faculty of Science and Engineering at the University of Manchester has invested in Early Career Researchers for many years, thereby increasing the diversity of our staff. These represent a strategic investment in outstanding researchers who will shape our future research portfolio. In 2026, we plan to appoint Dame Kathleen Ollerenshaw University Research Fellowships across the Faculty. These fellowships are fixed-term for 5 years and serve as an excellent stepping stone toward establishing an independent research career and securing a full-time, permanent academic position. It is therefore important that applicants discuss their applications with the candidate host departments and ensure that their teaching profile and skills align with the host departments' long-term strategy.

In 2026, we plan to appoint a Dame Kathleen Ollerenshaw University Research Fellowship aligned with FSE (viz., in the Department of Computer Science, and the Department of Mechanical and Aerospace Engineering), contributions to the Manchester BHF Centre of Research Excellence (CRE), with a focus on Theme 5: Computational modelling, simulation, and large language models.

AI for Science in Manchester BHF Centre of Research Excellence

The BHF CRE was established in 2024 to deliver high-quality scientific outputs that benefit global populations at risk of cardiovascular disease. It blends long-standing excellence in genomics and cardiac pathophysiology with emerging interdisciplinary strengths in artificial intelligence, data science and computational modelling, collectively referred to as AI for Science.

We are looking for outstanding candidates to undertake world-leading methodological research in the following areas, with an impact on cardiovascular medical devices and imaging, and regulatory science as part of the Manchester BHF CRE:

• Geometric Deep Learning for Complex Manifolds: Novel deep learning theories, models and architectures to simulate interactions within non-Euclidean, patient-specific cardiovascular structures.
• Generative Design & Latent Space Optimisation: Leveraging generative models and agentic architectures to automate the discovery of high-performance structural configurations of medical devices through high-dimensional latent space exploration.
• AI-Accelerated Multiphysics & Multiscale Solvers: Developing neural operators and hybrid AI integrators to bypass traditional computational bottlenecks in fluid-structure and durability simulations in cardiovascular modelling and medical device design.
• Stochastic Synthesis & Multimodal Data Fusion: Engineering robust frameworks for merging disparate data streams and generating high-fidelity synthetic populations for rigorous in-silico testing.
• Scientific Foundation Models & Continual Learning: Optimising LLM and agentic architectures for domain-specific scientific discovery through continual pre-training and instruction tuning on longitudinal datasets.
• Verifiable & Explainable Multimodal AI: Building robust, transparent AI systems that align diverse modalities with provable guarantees for safety-critical engineering applications.

Propelled by clinical challenges, defined by methodological breakthroughs. This is our bold ambition: to deliver breakthroughs in core foundational science in artificial intelligence and computational engineering that have a strong impact on patient lives. If you have an interdisciplinary mindset and are comfortable working across disciplines, this is your call. Manchester has several experts in cardiovascular modelling, imaging, and medical devices. Candidates with prior experience in regulatory science relevant to cardiovascular medical devices and imaging are particularly welcome.

APPLICATION PROCESS

Key contacts and information about the Faculty, Schools, Departments and Institutes can be found on our website (), and queries may be emailed to .

The University actively fosters a culture of inclusion and diversity and seeks to achieve true equality of opportunity for all members of its community. The Faculty is committed to having a representative workforce. Across the Schools, we hold Bronze and Silver Athena SWAN Awards, which recognise our commitment to equality, diversity, and inclusion, particularly the advancement of women’s education and careers in STEM.

The University also holds a Bronze Race Charter Mark recognising our commitment to improving the representation, progression and success of minority ethnic staff and students within higher education. In addition, we are a Disability Confident Employer, guaranteeing an interview to any disabled applicant who meets the minimum requirements for the role.