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.

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