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 (https://www.se.manchester.ac.uk/), and queries may be emailed to DKOFellows@manchester.ac.uk.

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.

As an equal opportunities employer, we welcome applicants from all sections of the community regardless of age, sex, gender (or gender identity), ethnicity, disability, sexual orientation and transgender status. We strongly encourage applications from individuals of all backgrounds, particularly those from underrepresented groups in academia. All appointments are made on merit. Our University is positive about flexible working – you can find out more here.

The University of Manchester

The University of Manchester (www.manchester.ac.uk) enjoys a global reputation for its research and innovative approach to learning, with ongoing £1 billion investment in facilities, staff, and buildings. This builds on our tradition of success that stretches back 200 years. The birth of the modern computer, the splitting of the atom, the founding principles of modern economics, the discovery of graphene, and the birthplace of engineering – these and many more world-changing innovations have their roots at our University. We are at the forefront of the search for solutions to some of the world's most pressing problems, boasting strong collaborative links with industry and public services. In 2025, the university ranked second in the Times Higher Education (THE) Impact Rankings, a table of more than 2,500 universities globally, for our impact in addressing the United Nations (UN) Sustainable Development Goals

(SDGs). The Dame Kathleen Ollerenshaw Fellowships are fully aligned with the recently launched Manchester 2035 strategy and our EDIA goals, incorporating Our People, Our Values, Our Future.

APPLICATION PROCESS

Key contacts and information about the Faculty, Schools, Departments and Institutes can be found on our website (https://www.se.manchester.ac.uk/) and queries may be emailed to DKOFellows@manchester.ac.uk.

Applications should be submitted via the application form on Jobtrain together with the following supporting documents noting the maximum page numbers for each document:

1. Curriculum Vitae (2 pages)
2. Please also upload your Research Proposal (2 pages), Academic Impact Statement (1 page) and Statement of Strategic Fit (1 page). These three documents must be combined into a single pdf (4 pages in total)

Please ensure you indicate which Department you are applying to

Deadline for applications: 23.59 (UK time) Monday 30 March 2026.

SELECTION CRITERIA

The criteria for appointment will be based upon:

• Overall potential of the fellowship to establish and maintain a distinctive and outstanding research and/or innovation activity
• The quality of the proposal, particularly in relation to potential impact and deliverables
• The relevance of the proposal to funders, industrial and/or policy stakeholders
• Potential to act as an ambassador and advocate in their field
• Ability to teach and communicate effectively with undergraduate and postgraduate students in the host department of choice

SELECTION PROCESS

Applications will be reviewed and shortlisted by a panel, independently chaired by a Head of School. A shortlist of candidates will be drawn and presented to the Faculty. An interview panel chaired by the Vice President and Dean of the Faculty will carry out the final interview.

Applications are particularly welcomed from women and Black, Asian and Minority Ethnic applicants who are currently under-represented at this level. Appointments will be made on merit. We will also support requests to hold the award part-time over a proportionately longer period. Research excellence is the main criterion on which these University Research Fellowships are awarded. However, career breaks as well as unconventional research career paths will be taken into account. Evaluations will be monitored to identify potential biases, e.g. in terms of gender and race, to guarantee transparency, fairness and impartiality in the treatment of proposals.

Full details relating to eligibility; appointment details/conditions; and application procedure are outlined in the Guidance Notes that accompany this scheme.

Key Dates

What you will get in return:

• Fantastic market leading Pension scheme
• Excellent employee health and wellbeing services including an Employee Assistance Programme
• Exceptional starting annual leave entitlement, plus bank holidays
• Additional paid closure over the Christmas period
• Local and national discounts at a range of major retailers

Our University is positive about flexible working – you can find out more here

Hybrid working arrangements may be considered.

Please be aware that due to the number of applications we unfortunately may not able to provide individual feedback on your application.

Please note that we are unable to respond to enquiries, accept CVs or applications from Recruitment Agencies.

Any recruitment enquiries from recruitment agencies should be directed to recruitmentservices.people@manchester.ac.uk

Any CV’s submitted by a recruitment agency will be considered a gift.

Enquiries about the vacancy, shortlisting and interviews:

Email: DKOFellows@manchester.ac.uk

General enquiries:

Email: recruitmentservices.people@manchester.ac.uk

Technical support:

0161 850 2004 https://jobseekersupport.jobtrain.co.uk/support/home

This vacancy will close for applications at midnight on the closing date.

Please see the link below for the Further Particulars document which contains the person specification criteria.