Computational Mechanics and Data-Driven Modelling of Composite Materials

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.

Eligibility

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.

Funding

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-free 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.

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

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

Before you apply

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.

How to apply

Apply online through our website: https://uom.link/pgr-apply-2425

When applying, you’ll need to specify the full name of this project, the name of your supervisor, if you already having funding or if you wish to be considered for available funding through the university, details of your previous study, and names and contact details of two referees.

Your application will not be processed without all of the required documents submitted at the time of application, and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered.

After you have applied you will be asked to upload the following supporting documents:

• Final Transcript and certificates of all awarded university level qualifications
• Interim Transcript of any university level qualifications in progress
• CV
• Supporting statement: A one or two page statement outlining your motivation to pursue postgraduate research and why you want to undertake postgraduate research at Manchester, any relevant research or work experience, the key findings of your previous research experience, and techniques and skills you’ve developed. (This is mandatory for all applicants and the application will be put on hold without it).
• Contact details for two referees (please make sure that the contact email you provide is an official university/work email address as we may need to verify the reference)
• English Language certificate (if applicable)

If you have any questions about making an application, please contact our admissions team by emailing FSE.doctoralacademy.admissions@manchester.ac.uk.

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact.

We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.

We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).