Summary
The Sustainable Energy Technology and Turbomachinery Laboratory (SETTL) within the Department of Mechanical Engineering at Imperial College London is seeking a postdoctoral Research Associate in AI for Turbomachinery Design. Funded by Mitsubishi Heavy Industries (MHI), this research project investigates how Artificial Intelligence (AI) and Machine Learning (ML) can accelerate engineering design processes, enable efficient exploration of complex design spaces, and drive innovation in sustainable turbomachinery for future energy and transportation systems. The postholder will collaborate closely with academic teams and industrial partners under the supervision of Dr. Teng Cao.
Designation
- Job Title / Designation: Research Associate (Postdoctoral Researcher)
- Job Category: Researcher / Academic
Key Job Details
| Parameter | Details |
| Job Reference | ENG04011 / Job ID: 28940 |
| Institution | Imperial College London |
| Faculty / Department | Faculty of Engineering / Department of Mechanical Engineering (Thermofluids Division) |
| Research Group | Sustainable Energy Technology and Turbomachinery Laboratory (SETTL) |
| Salary Range | £45,399 – £59,484 per annum |
| Contract Type | Full-Time, Fixed-Term (until October 31, 2027) |
| Expected Start Date | November 1, 2026 |
| Work Arrangement | Hybrid (approx. 60% on-site at South Kensington Campus) |
| Application Deadline | September 6, 2026 |
Research Area
- Artificial Intelligence (AI) and Machine Learning (ML) in Engineering Design
- Computational Fluid Dynamics (CFD) & Numerical Simulations
- Turbomachinery Design, Aerodynamics, and Fluid Dynamics
- Multi-objective Optimization and Surrogate Modeling for Clean Energy & Propulsion Systems
Location
- Campus: South Kensington Campus, London, SW7 2AZ, United Kingdom
- Working Pattern: Hybrid working arrangements available (typically 60% on-site presence).
Eligibility & Qualifications
- Education: Hold, or be near completion of, a PhD in Mechanical Engineering, Aerospace Engineering, Computer Science, Applied Mathematics, Physics, or a closely related quantitative engineering discipline.
- Technical Knowledge & Expertise: Strong background and practical experience in one or more of the following:
- Machine learning frameworks and generative/surrogate modeling
- Computational Fluid Dynamics (CFD) and aerodynamic performance analysis
- Turbomachinery design and fluid mechanics
- Research Track Record: Evidence of high-quality research outputs (peer-reviewed journal papers/conference proceedings) in fluid mechanics, CFD, machine learning, or turbomachinery.
- Skills: Proficient coding skills (e.g., Python, C++, CFD solver scripting), strong analytical ability, and excellent written and verbal communication skills to collaborate with industrial stakeholders and multidisciplinary teams.
Job Description & Responsibilities
- Conduct cutting-edge research integrating AI/ML frameworks with CFD workflows for turbomachinery component design and optimization.
- Develop, validate, and benchmark machine learning-based design and surrogate modeling tools.
- Execute high-fidelity numerical fluid simulations and evaluate aerodynamic/aerothermal performance.
- Manage, curate, and preprocess large datasets required for CFD training and ML inference.
- Prepare progress reports, deliver presentations, and actively engage with industrial project partners (Mitsubishi Heavy Industries).
- Author and co-author high-impact research papers for leading academic journals and international conferences.
How to Apply
- Online Application: Submit your application directly through the Imperial College London Recruitment Portal quoting job reference ENG04011.
- Required Documents: Complete the online application form and upload an up-to-date CV, a list of publications, and a cover letter detailing your relevant experience and research interests.
- Informal Inquiries: For technical or project-specific queries, contact Dr. Teng Cao via email at t.cao@imperial.ac.uk.
- Technical Support: For portal or application troubleshooting, email support.jobs@imperial.ac.uk.
Last Date for Apply
- Closing Date: September 6, 2026 (Advert may close early if a high volume of suitable applications is received, so early submission is encouraged).
