Research Assistant / Associate in Scientific Machine Learning

Job description
Job summary

Applications are invited for two Research Assistant / Associate positions, one based in the Department of Aeronautics and the other in the Department of Mathematics. These two posts are funded by a UKRI AI for Net Zero grant on (Real-time digital optimisation and decision making for energy and transport systems, EP/Y005619/1). The over-arching goal is to develop machine learning methods that are aware of the physics of the problem for the realization of real-time digital twins of engineering systems. Applications involve fluid mechanics and nonlinear partial differential equations.

You will develop physics-aware machine learning for engineering systems with fluids and nonlinear partial differential equations, while taking into account the efficiency and robustness of the machine learning algorithms. There will be possibilities of close interactions with the Alan Turing Institute, Imperial I-X, the Research Centre in Data-Driven Engineering, and the PIs’ relevant academic network in the UK, EU, and US. Funding is available for travelling and IT facilities for research-related tasks.

Duties and responsibilities

• Develop physics-aware machine learning methods for optimization of unsteady flows and nonlinear partial differential equations
• Disseminate research with peer-reviewed publications and conference presentations
• Assist other researchers and the Principal Investigators

Essential requirements

Research Associate:

PhD (or equivalent doctorate degree) in Engineering, Applied Mathematics, Computing, or a closely related discipline.

Research Assistant:

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

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

A full list of duties, responsibilities and person specification can be found in the job description.

Further information

These posts are full and fixed-term until 31 March 2025 and based at South Kensington Campus. 

Should you require any further details on the role please contact: Luca Magri [email protected]

For queries regarding the recruitment process please contact Lisa Kelly: [email protected]

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/

The College believes that the use of animals in research is vital to improve human and animal health and welfare. Animals may only be used in research programmes where their use is shown to be necessary for developing new treatments and making medical advances. Imperial is committed to ensuring that, in cases where this research is deemed essential, all animals in the College’s care are treated with full respect, and that all staff involved with this work show due consideration at every level. http://www.imperial.ac.uk/research-and-innovation/about-imperial-research/research-integrity/animal-research / 

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