About the Project
From wind moving through a wind farm to air circulating in buildings, turbulent flows affect energy efficiency, ventilation and the transport of heat and pollutants. Yet accurately predicting their behaviour remains a major challenge. Models used in everyday engineering design offer practical computation times but can lose accuracy in complex flows, while more detailed simulations are often too expensive for routine use.
This PhD will investigate how advances in machine learning can be combined with physical understanding to improve turbulence prediction. The aim is to develop models that are more reliable, physically interpretable and computationally efficient, helping engineers use simulation with greater confidence while advancing our understanding of turbulent flows.
You will develop expertise in computational fluid dynamics, scientific machine learning and the analysis of turbulence. Working with numerical simulations and high-quality experimental data, you will investigate the limitations of current approaches and assess new ideas against evidence from flows relevant to engineering practice. The project will suit a student interested in connecting mathematics, computing and physical reasoning to practical engineering problems.
The research could support improvements across aerospace, nuclear engineering, gas turbines, solar and wind energy, and biomedical devices. Research findings and software will be shared openly to support reproducibility and wider use.
The supervisory team brings together Hector Iacovides and Tim Craft's expertise in turbulence physics and modelling, Tim Tang's scientific machine learning research, and Ajay Harish's work on fluid–structure interaction and biomedical flows. Links with the UK Special Interest Group in Data-Driven Fluid Mechanics will connect the project to the wider research community.
This project is expected to start in September 2027.
Before you apply: We strongly recommend that you contact the supervisors for this project before you apply.
How to apply: To be considered for this project you must complete a formal application through our online application portal. If you already have an applicant account this link will directly open an application for PhD School of Engineering Scholarships. If you don't already have an applicant account, please follow the instructions here.
When applying, please specify the full title and supervisor/s of the project, details of your previous study, and names and contact details of two referees. You must also upload a Supporting Statement describing the motivation to apply to the project, your CV and transcripts of awarded and in-progress university qualifications. Please note late or incomplete applications will not be considered.
Equality, diversity and inclusion are fundamental to the success of The University of Manchester and central to all our activities. A diverse research community strengthens creativity, productivity and quality, while increasing the societal and economic impact of our work. We welcome applicants from all career paths, backgrounds and sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation or transgender status.
We welcome applications from candidates returning to study after a career break or experience in other roles. Flexible study arrangements may be available, including part-time study at 50%, 60% or 80%, subject to the requirements of the project and funder.
Eligibility: The standard academic entry requirement for this PhD is an upper second-class (2:1) honours degree in Engineering, Math, Physics, Computer Science (or international equivalent) OR any upper-second class (2:1) honours degree and a Master's degree at merit in Engineering, Math, Physics, Computer Science (or international equivalent).
This project will remain open until filled.
If your application is submitted by 1st November 2026, you can expect a decision by 18th December 2026.
If your application is submitted by 15th January 2027, you can expect a decision by 30th March 2027.
Self or externally funded students can also be considered for this project.
FSE_SoE
Funding Notes
Funding for this project covers tuition fees, UKRI minimum annual stipend (currently £21,805/annum) and up to a £2.5k/annum research training support grant for the full duration of the 3.5-year programme.