A fully funded PhD position is available at the University of Southampton in the field of aerodynamic design and fluidic topology optimisation for gas turbine components. The project aims to develop novel optimisation methods using machine and deep learning, combined with computational fluid dynamics, to revolutionise aerodynamic design processes. The research is based in the Rolls-Royce University Technology Centre for Computational Engineering and will focus on creating feature-based fluidic topology optimisation processes for gas turbine applications, such as combustor, blade, or seal features. The project is supervised by Professor David Toal, whose expertise includes design optimisation, automation, and machine learning. Applicants should have a strong background in engineering, mathematics, or computer science, with essential skills in computer aided design, computational fluid dynamics, and programming. Experience in gas turbine design and machine/deep learning tools is desirable. Funding is available for both UK and international students, with several studentships awarded on a rolling basis. The application deadline is July 31, 2025. Interested candidates should apply online, submitting a personal statement, CV, academic references, and transcripts, and naming Professor David Toal as supervisor. For further information, applicants can contact the doctoral college or Professor Toal directly.

The University of Southampton is offering a PhD position focused on developing novel fluidic topology optimisation methods for gas turbine design. The project aims to revolutionise aerodynamic design processes by integrating cutting-edge machine and deep learning techniques with computational fluid dynamics. The research will build on methods developed by the Rolls-Royce University Technology Centre for Computational Engineering, targeting applications such as combustor, blade, or seal optimisation. Applicants should have a strong background in engineering, mathematics, or computer science, with essential skills in computer aided design, computational fluid dynamics, and programming. Experience in gas turbine design and machine/deep learning tools is desirable. Funding opportunities, including studentships, are available for UK and international students, with awards made on a rolling basis. The application deadline is July 31, 2025. Interested candidates should apply online, submitting a personal statement, CV, academic references, and transcripts, and may contact Dr David Toal for further information.

The University of Southampton is seeking a PhD student to join a research project focused on developing novel fluidic topology optimisation methods for gas turbine design. The project aims to revolutionise aerodynamic design processes by integrating advanced machine and deep learning techniques with computational fluid dynamics. The research will build upon methods developed by the Rolls-Royce University Technology Centre for Computational Engineering, targeting the optimisation of gas turbine components such as combustors, blades, and seals. The successful candidate will work under the supervision of Professor David Toal, whose expertise includes design optimisation, automation, and computer-aided design. Applicants should have a strong background in engineering, mathematics, or computer science, with essential skills in CAD, CFD, and programming. Experience in gas turbine design and machine learning tools is desirable. Funding opportunities, including studentships, bursaries, and scholarships, are available for both UK and international students, with funding awarded on a rolling basis. The application deadline is July 31, 2025. Interested candidates should apply online, submitting a personal statement, CV, academic references, and transcripts, and naming Professor Toal as supervisor. For further information, applicants can contact the doctoral college or Professor Toal directly.