Research Assistant / Associate in Physics-aware machine learning for exascale fluid mechanics
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
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)
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.
For further details on the role please contact: Dr Luca MAGRI, email@example.com.
For queries regarding recruitment process - Lisa Kelly: firstname.lastname@example.org
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.
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