Research Associate in Digitalisation of Surface Manufacturing

Contract type: Fixed-term with a start date of 3 February 2025, and a fixed end date of 31 Jan 2026.

Location: Mappin Building

We are pleased to announce an exciting new opportunity to advance the state-of-the-art in digitalisation of surface manufacturing processes. This project will develop in-process monitoring techniques in coatings manufacturing process by adding sensors to capture the process data, followed by processing, fusion and analytics of sensor data to identify links between the process parameters and the resulting coating material characteristics.

The University of Sheffield is seeking to appoint a talented and highly motivated Research Associate in digitalisation of coatings manufacturing processes. The position is associated with funding from two projects: an EPSRC project: Intelligent engineering coatings for in-manufacture and in-service monitoring of critical safety products (EP/T024607/1) and the ATI project DELTA on Digitalising aerostructure assembly processes by capturing in-process data from assembly tasks. You will be joining a world-class team of digital manufacturing researchers, with frequent opportunities to engage in networking and development activities with other researchers across the consortium and to progress your career as a future research leader.

The role involves using a variety of sensing technologies such as spectrometer  sensors, infrared imaging, optical fibre sensors etc. for non-destructive direct measurement of coating material properties. You will also use material characterisation techniques for coating material analysis after the deposition process. You will then identify links between the sensor process data and the resulting coating material characteristics. The CoatIN project is a multi-disciplinary, multi-institution partnership bringing together the University of Manchester, the University of Sheffield, Cranfield University, Queen's University Belfast and Swansea University. The DELTA  project will involve utilisng machine learning to analyse the obtained in-process data from assembly tasks to gain insights that will drive improvements in productivity and assembly rates. The ability to work in a multi-disciplinary team and to write up findings for high-impact journals is very important.

We’re one of the best not-for-profit organisations to work for in the UK. The University’s Total Reward Package includes a competitive salary, a generous Pension Scheme and annual leave entitlement, as well as access to a range of learning and development courses to support your personal and professional development.

We build teams of people from different heritages and lifestyles from across the world, whose talent and contributions complement each other to greatest effect. We believe diversity in all its forms delivers greater impact through research, teaching and student experience.

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