Mechanical Systems Optimisation Engineer (KTP Associate)

Job Purpose
To develop an AI-enabled digital twin platform that models, monitors, and optimises Exwold Technology Ltd's chemical manufacturing processes in real time, improving efficiency, reliability, and sustainability. The project will embed advanced data analytics and predictive maintenance to reduce waste, enhance productivity, and support the UK's drive toward smart, sustainable manufacturing. Duties & Responsibilities- Project manage the delivery of the Knowledge Transfer Partnership work plan (with extensive support from both company and academic supervisors), which includes the following stages:

• Establish the foundation for the Physics-Embedded Machine Intelligence (PEMI) digital twin.
• Technology/ Methodology Familiarisation
• PEMI Digital Twin Prototype Development and Implementation.
• PEMI Digital Twin Scale-Up Across Four Sites – Multi-Site Validation, Model Adaptation & Predictive-Intelligence Extension
• PEMI Digital Twin Optimisation
• Full-Scale Digital-Twin Deployment and Operator Training
• Commercialisation and Sustainable Operation of the PEMI Digital Twin

- Co-author and present academic papers to relevant journals/conferences.- Contribute to KTP evaluation and final reports.- Adhere to the University’s & Exwold Technologies Ltd Health and Safety Policy and guidelines.- Adhere to the General Data Protection and The Data Protection Act 2018.- Promote Equality and Diversity for staff and students and embrace the University’s Values and Behaviours Framework.- Any other reasonable duties that may be allocated from time to time commensurate with the grading of the post. Person Specification ESSENTIAL-

• A master’s degree (or equivalent experience) in Mechanical Engineering, Mechatronics Engineering, or a closely related discipline.
• Experience in developing and applying physics-based or data-driven process models using tools such as MATLAB/Simulink, ANSYS, or OpenFOAM, focusing on manufacturing or process optimisation.
• Strong understanding of lean manufacturing principles, including value stream mapping, OEE (Overall Equipment Effectiveness), and continuous improvement methodologies for process efficiency and waste reduction.
• Knowledge of sensors for capturing mechanical system data, enabling the development of digital twin frameworks for predictive maintenance and performance optimisation.
• Knowledge of fundamental theories governing mechanical operations involved in granulation, mixing, and drying processes.
• Familiarity with machine learning techniques (e.g. regression, classification, time-series analysis) using Python or MATLAB, applied to process monitoring and control.
• Ability to work effectively both independently and as part of a multidisciplinary team, collaborating across engineering, production, and academic environments.
• Excellent problem-solving skills, with the ability to think critically and creatively, analyse complex data, and develop innovative solutions for real-world production challenges.

DESIRABLE:

• Demonstrated knowledge in physics-informed neural networks.
• Experience in in developing user-friendly interfaces for technical software tools.
• A PhD in a relevant field.

Job Type: Fixed term contract
Contract length: 36 monthsPay: £35,000.00-£39,000.00 per yearBenefits:

• Company pension
• Free parking
• On-site parking
• Relocation assistance
• Store discount
• UK visa sponsorship

Education:

• Master's (required)

Work Location: In person