EPSRC PhD Studentship: backwards to go forwards: Systems Engineering Approaches Inv

Area

Mechanical Materials & Manuf Eng

UK Other

Open Until Filled

ENG
306

Looking backwards to go forwards:
Systems Engineering Approaches for Inverse Design of Manufacturing Systems

Supervised by Rundong Yan, Alistair Speidel, and Rasa Remenyte-Prescott

This exciting opportunity is based within the Resilience Engineering Research Group at Faculty of Engineering which conducts cutting edge research into developing modelling techniques to predict ways of improving the design, maintenance, and operation of engineering systems in order to reduce the frequency and consequences of failure.

We are seeking a PhD student who is motivated to rethink how manufacturing systems are designed, moving beyond forward, trial-and-error approaches towards goal-driven, performance-led system design. The student will work at the intersection of systems engineering, modelling and simulation, and data-driven methods to develop an inverse design framework for manufacturing systems.

Together, we will advance the capability to design manufacturing systems that embed reliability, resilience, adaptability, and sustainability from the outset. By scientifically linking high-level performance objectives to system architecture and design decisions, this research aims to reduce costly late-stage redesign and enable manufacturing systems that can respond effectively to changing operational conditions. The outcomes of this work will support more efficient industrial design processes and contribute to the development of future manufacturing systems that are robust, reconfigurable, and fit for long-term operation.

Modern manufacturing systems are required to operate under increasing uncertainty, frequent change, and competing performance demands, including reliability, resilience, adaptability, and sustainability. However, current manufacturing system design approaches largely remain forward-driven: systems are designed, analysed, and only then assessed against these performance. At the same time, manufacturing is undergoing a major transformation driven by digitalisation, reconfigurable production, and the need for more sustainable and resilient operations.

These trends demand design methodologies that can explicitly account for performance goals from the outset, rather than treating them as afterthoughts. Despite advances in modelling, simulation, and data-driven optimisation, there is currently limited methodological support for systematically translating high-level performance objectives into concrete manufacturing system design decisions.

There is a clear need for new design approaches that enable engineers to reason backwards from desired system behaviour to feasible and robust system configurations across different operating environments and requirements. Addressing this gap will support the development of manufacturing systems that can better adapt to change, reduce costly redesign, and deliver sustained performance over their operational lifetime.

You will have the opportunity to develop a model-based systems engineering framework for the inverse design of manufacturing systems, enabling high-level performance objectives to directly inform system architecture and design decisions.

During the project, you will work closely with academic supervisors from both the Resilience Engineering Research Group and the Advanced Manufacturing Technology Research Group at the University of Nottingham, applying modelling, simulation, and data-driven methods to link high-level performance objectives to practical manufacturing system designs. You will develop and use advanced techniques, such as Petri nets and AI-based optimisation, to explore system behaviour and generate robust, adaptable, and sustainable manufacturing system configurations.

The project will involve applying these approaches to realistic manufacturing environments, allowing you to contribute to both methodological advances and industrially relevant case studies. This experience will prepare you for careers in advanced manufacturing, systems engineering, digital manufacturing, and research roles in academia or industry.

We are looking for an enthusiastic, self-motivated, and resourceful candidate with a strong interest in systems engineering, manufacturing systems, and modelling and simulation. You should be able to work independently as well as collaboratively, and be motivated to tackle open-ended research problems.

You should hold, or expect to obtain, a first-class or upper second-class (2:1) degree in a relevant discipline in engineering, science, or mathematics. Experience with modelling, simulation, optimisation, or programming (e.g. Python, MATLAB, C++, or similar) would be advantageous, though not essential, as learning and training will be expected during the PhD study.

After a suitable candidate is found, funding is then sought from the University of Nottingham as part of a…