Skip to main content Skip to navigation

#PhD Scholarship: Integrating machine learning and multiscale modelling for simulating fracture in materials with uncertainties

University of Warwick – School of Engineering

Qualification: Doctor of Philosophy in Engineering (PhD)

Start date: 1st April 2024 or 30th September 2024

Funding for: 3.5 years

Supervisor: Dr Emmanouil Kakouris and Dr Lukasz Figiel

Application deadline: The application deadline for this position is January 31, 2024. Prospective candidates are strongly encouraged to submit their applications at the earliest opportunity. The application process will be closed upon the identification of a suitable candidate.

Project Description:

Early detection of damage in materials is crucial, as cracks reduce local stiffness, can affect structural integrity, and accelerate the ageing process of physical assets. This project will help predict damage degradation in materials and enable mitigation measures to prevent potential failure of structural components, which are critical for ensuring safety and achieving societal objectives.

The aim of the project is to exploit the recent advances in machine learning (ML) and multiscale modelling for simulating damage in materials. With the increasing complexity of emergent materials, predictive structural damage models require mechanistic understanding across different material scales, i.e. multiscale modelling. This research will focus on modelling the link between the micro-material properties of engineering materials and their macroscale mechanical behaviour while retaining adequate precision and accuracy. ML tools will be utilised to pre-process massive amounts of data, integrate and analyse it from different input modalities and different levels of fidelity, identify correlations, and infer the non-linear response of the overall system. The project will focus on developing both deterministic and probabilistic frameworks to predict the response of structural components undergoing damage in real time. The probabilistic model will capture the uncertainties present in the data as well as in the ML-driven physics-based model.
We are looking for candidates to work at the confluence of structural mechanics, uncertainty quantification, and ML, towards addressing the safety and resilience challenges of an ageing, growing, and changing critical infrastructure.

The successful candidate should have an interest in computational material modelling, simulations, machine learning, and mathematics for solving partial differential equations. The candidate should have good programming skills (any of the followings Python, MATLAB, C/C++, FORTRAN or others).

The successful applicant will be situated within the School of Engineering and is encouraged to initiate the program at their earliest convenience.


The award will cover the tuition fees at the UK rate £4,712, plus a tax-free stipend of £18,622 per annum for 3.5 years of full-time study. International candidates are welcome to apply but would be required to meet the fee difference.


UK candidates with a first-class or 2.1 honours degree at BSc or MSc in engineering disciplines, applied mathematics, physical science or computational science and a strong interest in computational materials modelling, simulations, applied mathematics and machine learning. International students are welcome to apply but must meet the fee difference themselves.

How to apply:

Candidates should submit a formal application, details of how to do so can be found here 

Application form 'Course search':

Department: School of Engineering

Academic Year: 2023/24

Type of Course: Postgraduate Research

  • Engineering (MPhil/PhD) (P-H1Q2)

In the application form funding section, enter: Source: EK-Early Detection Machine Learning

If you wish to discuss any details of the project informally, please contact Dr Emmanouil Kakouris at

The University of Warwick provides an inclusive working and learning environment, recognising and respecting every individual’s differences. We welcome applications from individuals who identify with any of the protected characteristics defined by the Equality Act 2010.