The Role
AI/ML Research Engineer, Additive Manufacturing - KTP Associate
Faculty of Environmental Science and Economy
The starting salary will be from £34,630 to £40,704 on Grade (E) depending on skill set and experience.
The roleAn exciting opportunity has become available to work on a 36-month Knowledge Transfer Partnership (KTP) between the University of Exeter and Rapid Fusion Ltd. The KTP will develop software for intelligent process control in Large Format Additive Manufacturing (LFAM), focused on improving polymer deposition consistency and interlayer bonding performance. The candidate is expected to have a good understanding of the LFAM process including thermal behaviour of the polymers and knowledge of polymer material science.
The successful candidate will be employed by the University of Exeter and will spend the majority of their time working at Rapid Fusion Ltd in Exeter, with occasional visits to the University of Exeter. They will be expected to work full-time.
Company InformationRapid Fusion specialises in large-format additive manufacturing systems, including modular 3D printing platforms, AM modules, and robotic AM cells. Their LFAM technology enables the creation of large components with high production outputs, offering a sustainable alternative to traditional manufacturing. More information about the Rapid Fusion Ltd can be found here:
Previous applicants to this role need not apply.
Qualification / skills we require- Educated to first degree level or a good Master’s Degree in Mechanical Engineering, Materials Science, or in a related field of study or equivalent experience. A degree in Computer Science will be considered where supported by relevant experience in materials and manufacturing.
- Strong academic record demonstrating capability in theoretical analysis and practical application.
- Proficiency in Python and/or MATLAB for algorithm development, modelling and data analysis.
- Understanding of polymer materials and processing fundamentals, with familiarity with additive manufacturing processes.
- Knowledge of Finite Element Modelling (FEM) concepts; experience with ABAQUS or similar software desirable.
- Experience with machine learning frameworks (e.g. Tensor Flow, PyTorch).
- Knowledge of thermal analysis techniques (e.g. DSC, TGA) beneficial.
- Strong analytical and problem‑solving skills, with ability to interpret complex datasets.
- Excellent written and verbal communication skills, with the ability to explain complex technical concepts to mixed academic and industrial audiences.
- Ability to work effectively within a multidisciplinary academic–industry team, with commercial awareness.
- Experience of managing research projects or contributing significantly to collaborative research.
- Well‑organised, self‑motivated, and able to prioritise work independently within a project framework.
- Enthusiasm for hands‑on experimental work alongside computational modelling.
- Adaptable and able to work effectively in a fast‑paced SME environment.
- Understanding of health and safety requirements and good research record‑keeping practices.
- Commitment to equality, diversity and inclusive working practices.
- Willingness to be primarily based at Rapid Fusion Ltd (Exeter), with visits to the University of Exeter as required, and flexibility to meet project demands.
- A £6,000 training budget with 10% of your working time dedicated to personal development.
- A unique and challenging career opportunity, working with both industry and academia.
- For the right candidate there will be the opportunity to create an impactful and rewarding career with Rapid Fusion after this project is complete.
- The successful applicant will develop a wide range of industrial and research skills for their future career, utilising the…
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