Research Fellow / Senior Research Fellow in Digital Engineering for Gas Turbines
| Posting date: | 16 September 2025 |
|---|---|
| Salary: | £36,636 to £46,049 per year |
| Hours: | Full time |
| Closing date: | 07 October 2025 |
| Location: | Southampton, Hampshire |
| Remote working: | On-site only |
| Company: | University of Southampton |
| Job type: | Contract |
| Job reference: | 3208225DA |
Summary
The Rolls-Royce University Technology Centre for Computational Engineering is delighted to announce the recruitment of two research fellows. Working as part of a multi-partner Innovate UK funded project we are seeking highly motivated individuals to join us in the development of digital engineering technologies to accelerate the design of the next generation of gas turbine engines.
Successful candidates will have a PhD or equivalent in a relevant discipline and experience in the development of machine/deep learning (ML/DL) methods for engineering and will use this experience in collaboration with our industrial partners to develop methods to accelerate gas turbine design and improve through-life performance predictions. In both cases this will involve the development of novel ML/DL methods which leverage both computational and experimental data. Candidates should also have experience of numerical modelling (e.g. CFD and FEA), working as part of a team, with industry and disseminating their research within high impact journals and at international conferences.
This full-time post will be offered on a fixed-term contract until the 30th September 2028.
Informal enquiries may be addressed to Prof. David Toal, by email djjt@soton.ac.uk. Please note that applications sent directly to this email address will not be accepted.
We are committed to equality, diversity and inclusion and welcome applicants who support our mission of inclusivity.
Apply by 11.59 pm GMT on the closing date. For assistance contact Recruitment on +44(0)2380 592750 or recruitment@soton.ac.uk quoting the job number.
Successful candidates will have a PhD or equivalent in a relevant discipline and experience in the development of machine/deep learning (ML/DL) methods for engineering and will use this experience in collaboration with our industrial partners to develop methods to accelerate gas turbine design and improve through-life performance predictions. In both cases this will involve the development of novel ML/DL methods which leverage both computational and experimental data. Candidates should also have experience of numerical modelling (e.g. CFD and FEA), working as part of a team, with industry and disseminating their research within high impact journals and at international conferences.
This full-time post will be offered on a fixed-term contract until the 30th September 2028.
Informal enquiries may be addressed to Prof. David Toal, by email djjt@soton.ac.uk. Please note that applications sent directly to this email address will not be accepted.
We are committed to equality, diversity and inclusion and welcome applicants who support our mission of inclusivity.
Apply by 11.59 pm GMT on the closing date. For assistance contact Recruitment on +44(0)2380 592750 or recruitment@soton.ac.uk quoting the job number.
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