location: Boldrood campus

Rolls -Royce University Center for Technology for Mass Engineering is pleased to announce the employment of two research colleagues. By working as part of a multi -tape -fly -tape project in the UK, we are looking for very enthusiastic individuals to join us in developing digital engineering techniques to accelerate the design of the next generation of gas turbine engines.

Successful candidates will have a doctorate degree or its equivalent in discipline and relevant experience in developing deep learning methods (ML/DL) for engineering and they will use this experience in cooperation with our industrial partners to develop methods to accelerate the design of gas turbines and improve performance through. Either way, this will include the development of the new ML/DL methods that benefit from the arithmetic and experimental data. Candidates must also have experience in numerical modeling (such as CFD and FEA), and work as part of a team, with industry and publishing their research in high -influential magazines and international conferences.

This post will be presented full -time on a fixed -term contract for up to 30^Y September 2028.

Unofficial inquiries may be directed to Professor David Tall by email djjt@soton.ac.uk. Please note that the sent requests will not be accepted directly to this email address.

You must send the complete online application form by clicking the “App” button above.

location: Boldrood campus

Rolls -Royce University Center for Technology for Mass Engineering is pleased to announce the employment of two research colleagues. By working as part of a multi -tape -fly -tape project in the UK, we are looking for very enthusiastic individuals to join us in developing digital engineering techniques to accelerate the design of the next generation of gas turbine engines.

Successful candidates will have a doctorate degree or its equivalent in discipline and relevant experience in developing deep learning methods (ML/DL) for engineering and they will use this experience in cooperation with our industrial partners to develop methods to accelerate the design of gas turbines and improve performance through. Either way, this will include the development of the new ML/DL methods that benefit from the arithmetic and experimental data. Candidates must also have experience in numerical modeling (such as CFD and FEA), and work as part of a team, with industry and publishing their research in high -influential magazines and international conferences.

This post will be presented full -time on a fixed -term contract for up to 30^Y September 2028.

Unofficial inquiries may be directed to Professor David Tall by email djjt@soton.ac.uk. Please note that the sent requests will not be accepted directly to this email address.

You must send the complete online application form by clicking the “App” button above.