EPSRC CASE PhD Studentship

Eligibility criteria

Applicants should have a first-class Master's degree (or equivalent) in any field of engineering, physics or mathematics. Suitable backgrounds for these PhD positions include, but are not limited to, control engineering and mathematical optimization. They should be highly motivated individuals with a keen interest in conducting interdisciplinary research. Students must also meet the eligibility requirements for postgraduate studies at Imperial College London.

Course specific information

 

Application process

Please choose Electrical and Electronic Engineering Research Program and Control and Power Research Group, then indicate Prof. Eric Kerrigan as a potential supervisor when making the application. The application should include a covering letter, research statement and your CV. For queries regarding the application process, please contact eee.pgadmissions@imperial.ac.uk.

Additional information

A PhD Studentship in Optimal Control Strategies in Large-Scale Spatio-Temporal Maritime Systems Using Data-Driven Physics-Informed Machine Learning

Position Summary:

Applications are invited for a PhD studentship, to be undertaken at Imperial College London (Control and Power Research Group, Department of Electrical and Electronic Engineering). This studentship is funded by an EPSRC CASE award and industrial partner Andrew Moore & Associates. Andrew Moore and Associates are a leading global maritime consultancy with their UK office based in Sheffield. As part of the project, the student will work closely with staff at Andrew Moore & Associates and benefit from mentoring and supervision of their staff.

The project will be supervised by Prof. Eric Kerrigan (Professor of Control and Optimization, Imperial College London) and Dr Bryn Jones (Consulting Scientist, Andrew Moore & Associates).

Summary of Project:

Large-scale spatio-temporal systems, such as bulk grain transport and oil spill management, are vital to the maritime sector but present significant challenges in monitoring and control due to their vast scale and inherent uncertainties. This project aims to optimise sensor and actuator placement in these systems to minimise uncertainty and achieve specific control objectives by integrating feedback control with advanced data-driven and physics-informed approaches.

Objectives:

The project will enhance system modelling by utilising data assimilation techniques alongside reduced-order models to improve the accuracy of state estimations while efficiently capturing essential dynamics. It will optimise sensor and actuator placement by employing strategies that balance fixed and mobile deployments to minimise uncertainty. Robust control strategies will be developed by combining mathematical optimisation with modern computational techniques, incorporating real-time data processing for adaptive and responsive control. A trade-off analysis will evaluate the benefits of various sensor and actuator configurations, with theoretical models validated through simulations to provide practical guidelines.

Novelty and Expected Outcomes:

This project will introduce a novel integration of data-driven and physics-informed methodologies specifically tailored for large-scale maritime systems. By combining data assimilation with reduced-order modelling, it will enhance state estimation accuracy while maintaining computational efficiency. The optimisation strategies will uniquely address monitoring and control objectives simultaneously, balancing fixed and mobile deployments. The trade-off analysis will offer new insights into sensor and actuator configurations, providing practical guidelines not thoroughly explored in the literature. Expected outcomes include innovative monitoring and control strategies that significantly reduce system uncertainty and enhance performance, along with industry guidelines supported by simulation results.

Skills and Technologies:

Participants will develop proficiency in optimisation techniques, data assimilation, and the use of high-fidelity simulation tools for modelling complex systems. They will gain a deep understanding of feedback control systems and uncertainty quantification, with opportunities for collaboration with industry partners to ensure the research aligns with practical needs.

Why Join This Project?

This project offers a unique opportunity to make a tangible impact on the maritime industry by working at the forefront of optimisation, control theory, and engineering. It provides access to advanced computational resources, industry collaborations, and a pathway to developing highly sought-after skills in the rapidly evolving landscape of data-driven engineering solutions.

Funding:

This PhD studentship is jointly funded by the EPSRC and Andrew Moore & Associates. Only students with home fee status are eligible to apply for this studentship (Who pays home fees in England). It includes a EPSRC rate stipend of approximately £21,237* per year (tax-free) plus £5000 per year (tax-free) top-up from industry for four years, support of research expenses, travel to collaborators, conferences and a secondment at Andrew Moore & Associates. The student should ideally be able to start on the first day of the 2025/26 academic year (27 September 2025).

* Estimated values, confirmed annually by UKRI

Duties and responsibilities

The responsibilities include studying the relevant literature, defining the research problems based on the project descriptions, conducting independent research, regularly reporting progress and results in both oral and written format, collaborating with other team-members, and writing reports/papers of the research outcomes when appropriate. The successful candidate will be based at the Control and Power Group at Imperial College London, but will have the opportunity to visit Andrew Moore & Associates to attend meetings and undertake a secondment at their premises.

Contact

If you have any additional questions, please contact us at e.kerrigan@imperial.ac.uk.