PhD Studentship: AI Informed Surrogate Laser-Material Thermal Models based on High-Fidelity Multi-Physics Modelling and Test Data

Application deadline: All year round

Research theme: Applied Mathematics, Computational Metallurgy

UK only

This 3.5-year PhD project is fully funded and home students are eligible to apply. The successful candidate will receive an annual tax-free stipend set at the UKRI rate (£20,780 for 2025/26) and tuition fees will be paid. We expect the stipend to increase each year. The start date is October 2026.

We recommend that you apply early as the advert may be removed before the deadline.

The modelling of laser-material interactions is a complex multi-physics problem, very computationally intensive and often relies on detailed and expensive characterisation of the material's thermo-physical properties. This makes this type of analysis inaccessible for industrial applications that can require a wide range of modelling parameters, over large spatial and temporal spaces and where inputs are stochastic in nature. This is exacerbated in industrial applications that may include metals, ceramics and composite materials that require different physical modelling techniques.

The primary aim of the proposed research project is to create novel surrogate/reduced-order laser-material thermal models based on high-fidelity modelling and test data for both metals and thermo-set composite materials. To achieve this we will explore the use of advanced genetic algorithms and/or Artificial Intelligence (AI) methods that will also be applied to assess key material inputs for a range of problems of interest. Multiscale X-ray imaging from the Diamond Light Source Synchrotron facilities will be used to guide both detailed model validation as well as algorithms to determine key material data required by modelling.

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline. Successful candidates will be required to undertake Baseline Personnel Security Standard checks and potentially undergo Security Clearance.

To apply, please contact the main supervisor Dr Thomas Flint - thomas.flint@manchester.ac.uk. Please include details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project.