Research Associate in AI for Turbomachinery Design

The project aims to investigate and demonstrate how AI can accelerate engineering design processes, enable more efficient exploration of complex design spaces, and unlock new opportunities for innovation in turbomachinery design for future energy and transportation systems. Working closely with industrial stakeholders, the successful candidate will further develop, validate and apply AI-enabled design methodologies integrated with computational fluid dynamics (CFD) to support the generation, evaluation, and optimisation of turbomachinery components. The research outcomes will advance AI-enabled approaches to engineering design and support the development of more efficient and sustainable turbomachinery technologies.

This is an exciting opportunity to contribute to research at the intersection of AI, machine learning, CFD and turbomachinery, working with academic and industrial collaborators to develop innovative approaches to engineering design. The postholder will work with Dr Teng Cao in the Thermofluids Division of the Department of Mechanical Engineering.‌

What you would be doing:

• Undertake research in AI-enabled turbomachinery design, CFD and machine learning.
• Develop and apply machine learning-based design frameworks for turbomachinery applications.
• Conduct numerical simulations and analyse turbomachinery performance.
• Prepare and process data for machine learning and CFD workflows.
• Contribute to project reports, publications and presentations.
• Engage with the industrial sponsor and participate in project meetings.

What we are looking for:

• You should hold, or be close to completing, a PhD in a relevant discipline (such as Mechanical Engineering, Aerospace Engineering, Physics, Computer Science, Applied Mathematics, or a related field).
• You should have a strong background in one or more of the following areas: machine learning, CFD, turbomachinery, and fluid dynamics.
• You should have a track record of high-quality research publications in fluid mechanics, turbomachinery, machine learning, CFD, or related fields.
• You should have strong communication and interpersonal skills, with the ability to work effectively in both academic and industrial research environments.

What we can offer you:

• Working as part of a diverse, friendly, and supportive departmental team.
• Opportunity to work and network with a wide variety of College and Faculty teams.
• The opportunity to continue your career at a world-leading institution.
• Sector-leading salary and remuneration package (including 43 days off a year).

Further Information (College website only)

This position is fixed term and is expected to run until 31^st October 2027.

We anticipate that the successful candidate will start on 1^st November 2026.

If you require any further details on the role please contact: Dr Teng Cao at: t.cao@imperial.ac.uk.

Hybrid working may be considered for this role. Staff working in roles that are suitable for hybrid working will normally be expected to work 60% of their time onsite. The opportunity for hybrid working will be discussed at interview.

We reserve the right to close the advert prior to the closing date stated should we received a high number of applications. It is therefore advisable that you submit your application as early as possible to avoid disappointment.