Automation Engineer (KTP Associate)

The Robert Gordon University and John White & Son (Weighing Machines) Limited are seeking to recruit an Automation Engineer to work on a 36-month Knowledge Transfer Partnership (KTP) to design, develop and deploy an automated and adaptable weighing solution system for use with remotely operated weighbridges, across diverse industry sectors.

This system will include the integration of load cells, sensors, controllers and peripheral devices to realise customised control flow, data management and AI-based data analytic models.

The position will be based at John White & Son (Weighing Machines) Limited (JWS), a Fife based company working primarily with the Systems Engineering teams at JWS. The proposed project focuses on the development of an automated weighing system.

The KTP Associate will work in close partnership with staff from the School of Engineering at Robert Gordon University during the project.

The Knowledge Transfer Partnerships is a UK-wide programme that helps businesses to improve their competitiveness and productivity through the better use of knowledge, technology and skills that reside within the UK Knowledge Base. A Knowledge Transfer Partnership serves to meet a core strategic need and to identify innovative solutions to help that business grow.

This 36-month fixed term post is jointly funded by Innovate UK and John White & Son. KTP supports partnerships between businesses and universities or research organisations, placing KTP Associate to work on innovative high-profile projects.

This KTP Associate will benefit from a tax-free personal development budget of £6,000 (allowing them to participate in courses / professional qualifications) and will be responsible for other budgets including, travel and consumables.

The project is an interdisciplinary project and needs an Associate with a knowledge of engineering and embedded systems and strong programming skills. It requires a multidisciplinary approach embracing mechanical-automation and computing with strong capabilities in signal conditioning, data acquisition and artificial intelligence/machine learning (AI/ML) analytical skills required to achieve the project deliverables.

The Associate will come from a Mechatronic, Cloud Computing and AI/ML background with excellent mechanical and electronics experience and possess good knowledge of signals condition and acquisition. They will have a minimum of an MSc degree in Mechatronic or Cloud Computing and AI sciences or related field, and work experience in mechanical systems design, development, and commissioning. A PhD degree would be an added advantage.

For an informal discussion about the post please contact Prof. James Njuguna, j.njuguna@rgu.ac.uk, Tel. +44 (0) 1224 262304.

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