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EngD Studentship in the EPSRC Centre in Doctoral Training (CDT) in Innovation for Sustainable Composites Engineering

University of Nottingham

Qualification Type: PhD
Location: Nottingham
Funding for: UK Students
Funding amount: £26,780 - please see advert
Hours: Full Time
Placed On: 26th March 2026
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:

  • Investigate the multi-stage modelling approach for use with dry fibre forming and RTM manufacturing processes.
  • Assess if a modelling approach can inform process control and optimisation, leading to a reduced part scrap rate.
  • Build simulation tools that support the understanding of defect generation during forming.
  • Identify state-of-the-art software and methodologies for modelling composite manufacturing processes and demonstrate how they would be integrated within production facilities.
  • Excite your interest in creating manufacturing process models with a view to creating more sustainable aircraft parts.

This project is extremely well aligned to GKN Aerospace’s ambition of using out-of-autoclave technologies for composites manufacturing. You will:  

  • Be immersed in one of the world’s leading aerospace companies, spending approximately 75% of your time on-site. You'll gain real-world experience in industrial-scale composite manufacturing—exposure rarely available through academic programmes.
  • Gain hands-on experience with state-of-the-art composite materials and cutting-edge manufacturing techniques, preparing you for a career at the intersection of innovation and industry.
  • Deepen your passion for automated manufacturing processes, driving the development of sustainable composite structures for high-performance applications.
  • Undertake doctoral-level training at the University of Bristol, a globally recognised institution at the forefront of composites research.

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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