Closing date: 30 July 2019

Subject areas: Engineering

Start date: October 2019

Project description: Complex large-scale dynamical system often has a vast number of degrees of freedom. It is not possible to model the system sufficiently quickly to predict behaviours in real time. This project will extract effective data to construct a fast model using deep learning methods. Deep learning is part of a broader family of machine learning methods and has been applied to a number of fields.

The proposed PhD project offers the unique opportunity to develop a data-driven reduced order model (ROM) using deep learning methods. It provides a fast way of performing computationally intensive tasks with real-time speed for fluids problem. The proposed new ROM is constructed from a number of simulations representing different parameters such as different initial or boundary conditions. The deep learning methods are used to extract features from those simulations. After constructing the ROM, it is able to run the simulations with several orders of magnitude speedup.

Project supervisorDr Dunhui Xiao

For more details please see here:

Administrative Contacts & How To Apply

To apply, please complete and submit the following documents to Dr Dunhui Xiao (

International applicants may apply, but the funding source to cover the fee difference must be guaranteed and detailed in the covering letter and an IELTS result of 6.5 must be submitted.

For enquiries please contact Dr Dunhui Xiao (

Entry Requirements

Candidates should hold a first class honours degree and/or distinction at Master’s level in Computational mathematics, statistics, computer science, physics, or engineering.

Knowledge or experience of machine learning and fluid dynamics is an advantage (but not essential). Programming skills are essential.

We would normally expect the academic and English Language requirements (IELTS 6.5 or equivalent) to be met by point of application. For details on the University’s English Language entry requirements, please visit


Funding Notes

The scholarship covers the full cost of tuition fees and an annual stipend for UK/EU candidates but tuition fees only for international candidates.

UK/EU candidates: The scholarship covers the full cost of UK/EU tuition fees and an annual stipend of £14,296.

International candidates: The scholarship covers the full cost of tuition fees only.

There will be additional funds available for research expenses.

Funding Information

Application Deadline:

30 July 2019

Please see our website for how to apply:
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