Research Assistant or Associate in New data-driven optimization approaches for sensing in turbulent flows

Location: South Kensington

About the role:

The role will develop new AI methods for identifying the instantaneous state of a fluid flow from partial sensor information. The research will couple techniques from optimization and control theory, scientific machine learning, and partial differential equations to create a new approach for data-driven analysis of fluid flows. The successful applicant will have experience in one or more of these subject areas, and be able to creatively combine disciplines to make new research advances in fluid mechanics.

What you would be doing:

You will be creating data-driven algorithms which can solve state estimation problems in fluid mechanics, such as inferring the instantaneous state of a fluid’s velocity field from sensors embedded in its boundary. The research objective is to find the best way to embed simple partial differential equations into AI-based models to solve fluid sensing problems in a robust and efficient manner.

Your role may include developing new optimization techniques, coding new algorithms, creating new mathematical theory, and the analysis of large data ensembles. You will write papers for submission to academic journals, collaborate with academics and PhD students, and communicate your research at national or international conferences.

What we are looking for:

The successful applicant will have a keen enthusiasm for research in fluid mechanics, with a background in Engineering, Mathematics or a related discipline. Depending on experience and academic background, successful candidates should have experience in one or more of the following:

• Experience of data-driven modelling and optimization-based analysis.
• Knowledge of fluid mechanics.
• Knowledge of control theory and optimization.
• Knowledge of partial differential equations.
• Have a strong coding ability.

Research Associate: Hold a PhD in Engineering, Mathematics or a closely related discipline, or equivalent research, industrial or commercial experience.

*Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant.

Research Assistant: A first / master’s degree (or equivalent) in Engineering, Mathematics, or a closely related discipline.

What we can offer you:

• The opportunity to continue your career at a world-leading institution, in one of the prominent departments for fluid mechanics research in the UK
• Support for applying for follow-on positions and funding.
• Sector-leading salary and remuneration package (including 39 days off a year)

Further Information

If you require any further details on the role, or would like an informal discussion prior to application, please contact: Dr Andrew Wynn – a.wynn@imperial.ac.uk

Please note that job descriptions are not exhaustive, and you may be asked to take on additional duties that align with the key responsibilities mentioned above.

If you encounter any technical issues while applying online, please don't hesitate to email us at support.jobs@imperial.ac.uk. We're here to help.

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