Research Fellow / Senior Research Fellow in Digital Engineering for Gas Turbines

UNIVERSITY OF SOUTHAMPTON; United Kingdom; On-site; GBP 35,000 - 50,000; Full time

Job summary

A renowned research university in the United Kingdom is seeking two research fellows to join the Rolls-Royce University Technology Centre for Computational Engineering. The successful candidates will develop digital engineering technologies to enhance gas turbine design and reliability. A PhD or equivalent experience in machine/deep learning methods and numerical modelling is essential. This full-time position is fixed-term until September 2028, offering collaboration with industry and opportunities for high-impact research dissemination.

Qualifications

• Experience in machine/deep learning methods for engineering.
• Experience in numerical modelling, e.g., CFD and FEA.
• Ability to disseminate research within high impact journals.

Responsibilities

• Develop ML/DL methods to accelerate gas turbine design.
• Collaborate with industrial partners for method development.
• Contribute to research dissemination through publications and conferences.

Skills

Machine/deep learning methods

Numerical modelling (CFD and FEA)

Collaboration and teamwork

Education

PhD or equivalent in a relevant discipline

Job description

Organisation/Company UNIVERSITY OF SOUTHAMPTON Research Field Computer science Engineering Researcher Profile First Stage Researcher (R1) Country United Kingdom Application Deadline 7 Oct 2025 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

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 30thSeptember 2028.

Informal enquiries may be addressed to Prof. David Toal, by email [e-mail] . Please note that applications sent directly to this email address will not be accepted.