PhD Studentship: DATAAM-F1: Design – Additive – Test – Adapt: A Digital Twin Framework for Data-Driven Additive Manufacturing in Formula 1

Project advert

In collaboration with the Aston Martin Aramco Formula One Team, this is an opportunity to investigate how additive manufacturing parameters influence the aerodynamic performance of wind tunnel components in elite motorsport. This interdisciplinary project will develop a data-driven approach to understand and predict how process settings, build orientation, machine variability, and material properties affect dimensional accuracy and aerodynamic behaviour, ultimately improving the reliability of aerodynamic testing. The project combines additive manufacturing, data analytics and fluid dynamics to bridge the gap between manufacturing and aerodynamic testing, supporting the next generation of high-performance engineering. 

What you’ll do:

• Investigate the relationship between manufacturing parameters (build orientation, resin choice, post-processing) and aerodynamic performance in wind tunnel tests.
• Develop an integrated performance model linking process data, 3D metrology, and wind tunnel results, allowing for predictive insights into the impact of manufacturing deviations on aerodynamic performance.
• Gain hands-on experience with state-of-the-art technologies, including stereolithography (SLA) printing and wind tunnel testing at Aston Martin Aramco Formula One and Manchester Met’s PrintCity.
• Collaborate with experts in additive manufacturing, fluid dynamics, and data science, producing impactful research publications while advancing knowledge at the intersection of engineering and motorsport. 

Project aims and objectives

• Investigate the impact of SLA process settings (build orientation, resin type, post-processing) on geometric fidelity and aerodynamic behaviour.
• Develop an integrated performance model combining SLA process data, metrology, and wind tunnel results to predict manufacturing-induced aerodynamic deviations.
• Explore machine-to-machine variability and evaluate how local and systemic errors affect part quality.
• Enhance CAD-to-print workflows to capture and mitigate sources of manufacturing variation.
• Provide recommendations for process optimization and compensation strategies to improve repeatability and accuracy in high-performance applications. 

Funding

Only Home students can apply. Home tuition fees will be covered for the duration of the three-year and six-month award, which is £5,006 for the year 2025/26. 

The student will receive a standard stipend payment for the duration of the award. These payments are set at a level determined by the UKRI, currently £20,780 for the academic year 2025/26. 

Specific requirements of the candidate

The ideal candidate should meet some of the following:

• A minimum of an honours degree at first or upper second class (2:1) level (or equivalent) in Mechanical Engineering, Manufacturing or Automotive Engineering, or a related discipline.
• A strong understanding of additive manufacturing, particularly stereolithography (SLA), and/or fluid dynamics or aerodynamics.
• Experience with 3D CAD modelling, manufacturing and the ability to work with metrology equipment (e.g., 3D scanning, surface inspection).
• Familiarity with data analysis tools (e.g., MATLAB, Python, or similar) and basic knowledge of machine learning is an advantage.
• Familiarity with research methodologies, including experimental design and data-driven modelling

How to apply

Interested applicants should contact Carl Diver (c.diver@mmu.ac.uk) for an informal discussion. 

To apply you will need to complete the online application form for a full-time PhD in Engineering.

Please include your CV and a cover letter addressing the project’s aims and objectives, demonstrating how the skills you have map to the area of research and why you see this area as being of importance and interest. 

If applying online, you will need to upload your statement in the supporting documents section, or email the application form and statement to PGRAdmissions@mmu.ac.uk. 

Closing date: 18 December 2025

Expected start date: April 2026

Please quote the reference: SciEng-CD-April 2026-Aston Martin Motorsport 

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