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

University of Southampton; Eastleigh; On-site; GBP 30,000 - 40,000; Full time

Job summary

A leading university in the UK seeks two research fellows to develop digital engineering technologies. The role involves creating ML/DL methods for gas turbine design and collaborating with industrial partners. Candidates should hold a PhD and have experience in relevant engineering methods. This is a full-time, fixed-term position until September 2028 with a focus on research dissemination in high-impact journals.

Qualifications

• PhD or equivalent in a relevant discipline.
• Experience in the development of machine/deep learning (ML/DL) methods for engineering.
• Experience in numerical modelling (e.g., CFD and FEA).

Responsibilities

• Develop novel ML/DL methods for gas turbine design.
• Collaborate with industrial partners on research.
• Disseminate research in high-impact journals.

Skills

Machine Learning (ML)

Deep Learning (DL)

Numerical Modelling

Collaboration with Industry

Research Dissemination

Education

PhD or equivalent in a relevant discipline

Tools

Computational Fluid Dynamics (CFD)

Finite Element Analysis (FEA)

Job description

Overview

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.

Responsibilities

• Develop novel ML/DL methods which leverage both computational and experimental data to accelerate gas turbine design and improve through-life performance predictions.
• Collaborate with industrial partners to develop methods that accelerate gas turbine design and improve through-life performance predictions.
• Develop ML/DL methods for engineering in collaboration with industrial partners and disseminate research within high-impact journals and at international conferences.
• Utilise numerical modelling (e.g., CFD and FEA) and work as part of a team on a multi-partner project.

Qualifications

• PhD or equivalent in a relevant discipline.
• Experience in the development of machine/deep learning (ML/DL) methods for engineering.
• Experience in numerical modelling (e.g., CFD and FEA).
• Experience working as part of a team, with industry, and disseminating research within high-impact journals and at international conferences.

Contract details

This full-time post will be offered on a fixed-term contract until the 30th September 2028.

Enquiries

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.

Equality, diversity and inclusion

We are committed to equality, diversity and inclusion and welcome applicants who support our mission of inclusivity.

How to apply

Apply by 11.59 pm GMT on the closing date. For assistance contact Recruitment on [phone] or [e-mail] quoting the job number.