The role
The University of Bristol invites applications for a Postdoctoral Researcher to join the EPSRC-funded BladeUp Prosperity Partnership, a collaboration with Vestas, the world’s largest manufacturer of wind turbine blades and wind turbines, and LMAT, a specialist SME in composites process modelling. The project focuses on the design, manufacture and performance of next-generation wind turbine blades, aiming to enable faster, cheaper, and more robust production through machine learning, multi-scale modelling, and advanced process simulation.
The successful candidate will be based at the Bristol Composites Institute, the UK’s largest university-based composites research centre, and will work closely with leading academic and industrial partners. You will contribute to the development of computational tools for the design and optimisation of large-scale wind turbine blades, helping to deliver new engineering methods into industrial practice.
We are seeking a researcher with expertise in:
1. Stochastic Structural Design, e.g. robust design under uncertainty and reliability analysis of full-scale wind systems,
complementing established project activities in:
2. multi-scale finite-element modelling of manufacturing defects and fatigue;
3. simulation and optimisation of liquid composite moulding and curing processes;
4. advanced modelling techniques for large-scale composite simulation and integration with research and industrial software environments.
We welcome applications from candidates from all backgrounds and are committed to building a diverse, inclusive, and supportive research environment.
Applicants should have expertise in two or more of the following areas: computational mechanics, composite manufacturing, stochastic design, aeroelasticity, wind turbine engineering, or multi-scale modelling. Experience with relevant software tools (e.g. Abaqus, ANSYS, MATLAB, Python) and an interest in developing industry-ready research tools are strongly encouraged.
What will you be doing?
5. Conducting high-impact research in composite and structural modelling, and stochastic design, contributing to the future of blade manufacturing.
6. Collaborating with leading academic and industrial teams to co-develop next-generation design and optimisation tools.
7. Analysing large experimental and simulation datasets to improve blade performance, manufacturability, and reliability.
8. Publishing your findings in top journals and presenting at international conferences, with opportunities to contribute to tool integration and industrial implementation.
You should apply if
9. You have a PhD (or are near completion) in engineering, materials science, computational mechanics, or a closely related field.
10. You have expertise in one or more of the following: finite element modelling, composite manufacturing, machine learning, or stochastic design.
11. You are motivated to work across academic–industrial boundaries and contribute to practical solutions for clean energy technologies.
12. You enjoy collaborative, interdisciplinary research and are eager to publish, present, and translate your work into real-world impact.