"PhD Studentship: Smart Manufacturing and Digital Twin Modelling for Remanufacturing Systems (PhD (Funded)"

Remanufacturing plays a vital role in enabling sustainable and circular production systems by recovering value from end-of-use products and components. A key operational stage within remanufacturing is product recovery and material separation, which strongly influences overall system performance, cost efficiency, and environmental impact. This is a critical stage in remanufacturing where uncertainties make planning and decision-making more complex and difficult to optimise using conventional approaches. This project aims to develop advanced modelling and simulation frameworks to support decision-making in smart remanufacturing systems, enabling improved operational performance, resource efficiency, and sustainability outcomes.

Alongside the Exeter Digital Enterprise Systems (ExDES) research group, the successful candidate will be expected to:

• Develop a conceptual modelling and digital twin framework for smart remanufacturing systems to enhance decision-making under uncertainty.
• Design and implement the simulation models using appropriate tools (e.g., AnyLogic, Simio, Siemens Plant Sim, Python, MATLAB or other relevant simulation platforms)
• Evaluate and validate the proposed framework and assess its impact on operational efficiency and sustainability in remanufacturing contexts

This funded PhD studentship is open to highly motivated candidates with a strong background in Engineering or a closely related discipline. Applicants should have an interest in manufacturing systems, operations research, and simulation modelling.

Candidates with prior experience in areas such as discrete-event simulation, agent-based modelling, systems modelling, digital twins, optimisation, or decision-support systems are especially encouraged to apply.

Applicants should hold (or expect to obtain) a first-class or strong upper second-class undergraduate degree (or international equivalent) in Engineering, Industrial Engineering, Manufacturing Engineering, Systems Engineering, Operations Management, Computer Science, or a related field. A master's degree in a relevant area would be desirable but is not essential.