University of Southampton
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PhD in genuinely high-order discretisation of Navier-Stokes equations using machine learning for turbomachinery aeroacoustics University of Southampton in United Kingdom
Degree Level
PhD
Field of study
Computer Science
Funding
Competition funded PhD project. Funding details are not specified in the post, but the project is presented as a funded postgraduate research opportunity.
Deadline
Sep 30, 2027
Country
United Kingdom
University
University of Southampton

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About this position
University of Southampton is advertising a competition-funded PhD project titled “Genuinely high-order discretisation of Navier-Stokes equations using machine learning and its application to turbomachinery aeroacoustics.”
The project sits in the Faculty of Engineering and Physical Sciences and focuses on computational fluid dynamics, Navier-Stokes equations, high-order discretisation, machine learning, neural networks, and turbomachinery aeroacoustics. The research aims to build a more stable and genuinely high-order numerical framework, integrate it into the CANARD code developed at Southampton, and demonstrate it on challenging aeroacoustics problems in partnership with Rolls-Royce plc.
The work will involve massively parallel computing on UK supercomputing resources, including CIRRUS and IRIDIS-7. Training in the main code and supercomputing will be provided at the start, and the student will also have opportunities to attend taught courses and external workshops in machine learning and neural networks.
Entry requirement: a 2:1 honours degree. The page does not list additional language or test waivers. The closing date shown on the project page is 30 September 2027.
This is a strong fit for applicants interested in mechanical engineering, aerospace engineering, numerical methods, CFD, and AI-assisted scientific computing.
Funding details
Competition funded PhD project. Funding details are not specified in the post, but the project is presented as a funded postgraduate research opportunity.
What's required
Applicants should normally hold a 2:1 honours degree. The project is suited to candidates interested in computational fluid dynamics, numerical methods, machine learning, neural networks, and high-performance computing; training in CANARD and massively parallel supercomputing will be provided.
How to apply
Review the project page and use the university's PhD application process linked from the page. Check the full entry requirements and fees/funding sections before applying. Submit your application before the closing date.
More information can be found here
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