Research Assistant / Associate in Physics-aware machine learning for exascale fluid mechanics

Job summary

Applications are invited for a fully funded 30-month position at the Research Assistant / Associate level (informally known as “Post-doc”) within the Physics-aware Data Assimilation and Machine Learning Group (PI: Luca Magri) in the Department of Aeronautics at Imperial College London. The position is funded by the EPSRC ExCALIBUR project “Turbulence at the Exascale: Application to Wind Energy, Green Aviation, Air Quality and Net-zero...

Job listing information

  • Reference ENG01984
  • Date posted 24 January 2022
  • Closing date 23 February 2022

Job description

Job summary

Applications are invited for a fully funded 30-month position at the Research Assistant / Associate level (informally known as “Post-doc”) within the Physics-aware Data Assimilation and Machine Learning Group (PI: Luca Magri) in the Department of Aeronautics at Imperial College London. The position is funded by the EPSRC ExCALIBUR project “Turbulence at the Exascale: Application to Wind Energy, Green Aviation, Air Quality and Net-zero Combustion”. The project is a collaboration led by Imperial College London together with the universities of Warwick, Cambridge, Newcastle, Southampton, and the Daresbury Laboratory. The post holder will develop physics-aware machine learning for the optimization of engineering systems with fluids. Funding is available for travelling and IT facilities for research-related tasks. The Research Associate is expected to produce results suitable for presentation in international conferences and publication in leading peer-reviewed journals/conferences.

The over-arching goal is to develop machine learning methods that are aware of the physics of the problem with a focus on exascale computing. Applications involve fluid mechanics. More information on the PI’s research can be found here: https://www.imperial.ac.uk/people/l.magri.

Duties and responsibilities


  • Develop physics-aware machine learning methods for optimization of unsteady flows taking advantage of GPUs

  • Disseminate research with peer-reviewed publications and conference presentations (with the PI)



  • Contribute to adding new capabilities to the Xcompact3d framework (uncertainty quantification, machine learning algorithms)

Essential requirements

Experience in fluid mechanics and machine learning and/or data assimilation.

Those appointed at Research Associate level

PhD (or equivalent doctorate degree) in Engineering, Applied Mathematics, Computing, or a closely related discipline with experience in high performance computing, fluid mechanics and machine learning.

Those appointed at Research Assistant level

A first / masters degree (or equivalent) in Computer Science, Engineering, Applied Mathematics, Computing, or a closely related discipline.

Further information

For further details on the role please contact: Dr Luca MAGRI, l.magri@imperial.ac.uk.

For queries regarding recruitment process - Lisa Kelly: l.kelly@imperial.ac.uk

Candidates who have not yet been officially awarded their PhD will be appointed as a Research Assistant within the salary range £36,694 - £39,888 per annum.

The College is a proud signatory to the San-Francisco Declaration on Research Assessment (DORA), which means that in hiring and promotion decisions, we evaluate applicants on the quality of their work, not the journal impact factor where it is published. For more information, see https://www.imperial.ac.uk/research-and-innovation/about-imperial-research/research-evaluation/

 

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About Imperial College London

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Additional information

Please note that job descriptions cannot be exhaustive, and the post-holder may be required to undertake other duties, which are broadly in line with the above key responsibilities.

Imperial College is committed to equality of opportunity and to eliminating discrimination. All employees are expected to follow the Imperial Values & Behaviours framework. Our values are: 

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  • Innovation

In addition to the above, employees are required to observe and comply with all College policies and regulations.

We are committed to equality of opportunity, to eliminating discrimination and to creating an inclusive working environment for all. We therefore encourage candidates to apply irrespective of age, disability, marriage or civil partnership status, pregnancy or maternity, race, religion and belief, gender identity, sex, or sexual orientation. We are an Athena SWAN Silver Award winner, a Disability Confident Leader and a Stonewall Diversity Champion.

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