PhD in Computational Mechanics and Data-Driven Modelling of Composite Materials

Deadline: All year round

Theme: Computational Mechanics

This 3.5-year PhD project is fully funded; students who are eligible to pay tuition fees at the Home rate are eligible to apply. The successful candidate will receive an annual tax-fee stipend set at the UKRI rate (£21,805 for 2026/27) and tuition fees will be paid. We expect the stipend to increase each year. The start date is January 2027.

International applicants are welcome to get in touch if they hold or intend to apply for external funding.

Fibre-reinforced polymer composites are widely popular across aerospace, automotive, energy, and sports engineering thanks to their high specific properties. Realising their full potential, and meeting growing performance and sustainability demands, depends on understanding how manufacturing-induced defects affect their behaviour. These defects occur at several scales: voids, cracks and misaligned fibres at the microscale, and misaligned plies at the mesoscale. The development of next-generation composites requires a better understanding of how these defects affect their mechanical performance.

The successful candidate will work on one of the following directions, and the final scope will be shaped based on their interests and skills:

• Defect-informed damage modelling: advanced computational models informed by real microscopic defects to investigate their influence on damage initiation and progression, and improve the prediction of composite performance;
• Interpretable, physics-informed machine learning: data-driven methods that respect the underlying mechanics, to accelerate structure–property predictions and support quality assessment of manufactured composites;
• Multiscale modelling: a framework that accounts for defects across length scales and captures the interaction between constituent-level and ply-level behaviour.
• The investigation will build on in-house computational frameworks developed within the research group, which enable large-scale composite microstructures reconstructed from X-ray computed tomography data to be incorporated into mechanistic finite element models.
• The outcome of this research aids a more efficient composite design by reducing over-conservative sizing and enables a wider adoption of emerging sustainable composites, whose manufacturing variability is often a barrier.

What you will gain

You will develop expertise in computational mechanics, nonlinear finite element analysis, and data-driven modelling. You may also gain experience in multiscale modelling, image-based modelling, and machine learning for solid mechanics.

You will have access to world-class facilities at the University of Manchester and the Henry Royce Institute. You will also join a collaboration network spanning Imperial College London, University of Bristol, King's College London and other departments at Manchester.

We are seeking a motivated candidate with a strong academic background and a passion for mechanics of materials. You should hold, or expect to obtain, at least a 2.1 honours degree (or equivalent) in Mechanical Engineering, Computer Science or a closely related discipline.

A background or interest in one or more of the following areas is particularly desirable:

• Mechanics of composites or multiphase materials;
• Data-driven modelling and machine learning;
• Computational mechanics.

You should also demonstrate at least one of these technical skills:

• Finite element modelling (implicit or explicit), e.g., using Abaqus, Ansys, or COMSOL;
• Deep learning-based approaches;
• Programming (e.g., Python or MATLAB);

Above all, you should be enthusiastic, self-motivated, strong communicator, and meet the University of Manchester's academic requirements for PhD enrolment.

For further information about the project or any informal enquiries, please contact Dr Dimitrios Bikos, dimitrios.bikos@manchester.ac.uk.

Please include a CV, details of your current studies, academic background, and any relevant experience. For international candidates, please also include your funding plan.

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