| Qualification Type: | PhD |
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| Location: | Nottingham |
| Funding for: | UK Students |
| Funding amount: | £26,780 - please see advert |
| Hours: | Full Time |
| Placed On: | 26th March 2026 |
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| Closes: | 17th April 2026 |
This opportunity adds to the portfolio of projects within the new CDT for Innovation for Sustainable Composites Engineering launched in 2024: www.bristol.ac.uk/composites/cdtsustainablecompeng.
Research focus areas: Mechanical Engineering, Civil Engineering, Aerospace Engineering, Design Engineering, Materials Science
Scholarship Details: An enhanced stipend of £26,780 for 2026/27, home tuition fees and generous financial support for research and training for the successful candidate.
Duration: 4 years
Eligibility: Home/permanent UK residents
Start Date: September 2026
Candidate Requirements: Applicants must hold/achieve a minimum a 2:1 MEng or merit at Masters level or equivalent in engineering, physics or chemistry, depending on the project. Applicants without a master's qualification may be considered on an exceptional basis, provided they hold a first-class undergraduate degree. Please note, acceptance will also depend on evidence of readiness to pursue a research degree and performance at interview.
Project Description (EngD): EngD on Multi-Stage Simulation of Aerospace Out-of-Autoclave Composite Manufacture sponsored by GKN Aerospace (Supervised by Dr Mikhail Mateev and Prof Lee Harper)
Resin transfer moulding (RTM) provides a route to reduce the energy wastage in composites manufacturing. It enables components to be produced quickly without the use of an autoclave. For complex geometries, such as those used in wing spars of considerable length, a dry carbon fibre fabric needs to be formed to shape before injecting the polymer matrix. During dry fabric forming, defects such as wrinkles can occur, which cause parts to be scrapped. To minimise material and energy wastage, digital models of the manufacturing processes can be developed and linked to process control and optimisation. State-of-the-art digital models and AI tools that incorporate machine learning could enable predictions of the dry fibre forming that are subsequently used as input into the RTM process model. The EngD project will:
This project is extremely well aligned to GKN Aerospace’s ambition of using out-of-autoclave technologies for composites manufacturing. You will:
Eligibility: Home/permanent UK residents subject to security clearance
To apply please submit a personal statement, outlining your experience and your interests in this EngD project, plus your CV and transcript to mikhail.matveev@nottingham.ac.uk
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