About this position
Despite significant advances in numerical techniques and computing hardware, the high computational cost of large-scale 3D computational fluid dynamics (CFD) modelling remains a major challenge. A new approach has emerged that integrates data and mathematical models through neural networks. This has led to the development of a method for solving partial differential equations (PDEs) known as physics-informed neural networks (PINNs).
However, these approaches are still in their early stages of development and have yet to demonstrate their effectiveness for complex real engineering problems. This project proposes the development of a new CFD simulator for offshore renewable energy applications based on physics-informed deep learning that offers greater efficiency and robustness.
This is a unique and exciting opportunity to work in an excellent research group known for its long record of accomplishment in delivering outstanding research in marine hydrodynamics and computational fluid dynamics and their applications in both conventional and renewable offshore energy.
Objectives
- To reduce computational time during the training process, a linear solution based on potential flow theory is used as the training dataset for the neural networks.
- A PINN model is developed for the potential flow model to obtain up to second-order nonlinear solutions for water wave interactions with marine structures.
- The developed PINN is further integrated into the open-source software package OpenFOAM, ultimately demonstrating its effectiveness in simulating offshore renewable energy systems.
Funding
These are doctoral teaching assistant positions that combine a PhD programme with a university teaching contract. Your time will be split approximately 60% on research and 40% on teaching. This provides excellent preparation for candidates considering an academic career at a university.
The teaching component will typically run over the 22 teaching weeks per year and the four assessment weeks. You will help deliver an outstanding student experience by supporting lead academics with classroom and lab teaching and assessment, further building the skills developed within your PhD research programme.
The position is grade 6 with a current salary of £31,236 and includes payment of home PhD tuition fees for the duration of the 6-year award. Home students can apply. Applicants must have the right to work in the UK. We are unable to offer visa sponsorship for this role.
Candidate requirements
The successful candidate should have a good honours degree or a master’s degree in computer science, mathematics, civil engineering, mechanical engineering, naval architecture, or a relevant discipline.
Essential
- Strong programming skills.
- Excellent communication and teamwork abilities.
- Capacity to present research findings at research meetings and conferences, and through journal publications.
Desirable
- Knowledge or experience in Artificial Intelligence or hydrodynamics.
- Experience in teaching.
How to apply
If you have any questions, contact the principal supervisor, Dr Wei Bai .
To apply you will need to complete the online application form for a part time PhD in Mathematics.
Please complete the Doctoral Project Applicant Form , and include your CV and a covering letter to demonstrate how your skills and experience map to the aims and objectives of the project, the area of research and why you see this area as being of importance and interest.
Please upload these documents in the supporting documents section of the University’s Admissions Portal or send them to the PGR Admissions team at pgradmissions@mmu.ac.uk .
Please quote the reference: SciEng-DTA Jan 2027-WB- PINN based CFD
Funding
GBP 31,236 per year
How to apply
- Read the full advert on the source site — it carries the authoritative terms.
- Email the contact below with your CV and a short, specific message. See Emailing Professors.
- Prepare your SOP, CV, transcripts and referees before the deadline.
- Apply through the university's own portal. Never pay a fee to a third party.