This PhD project aims to improve the performance of wave energy converter arrays by developing intelligent control strategies that allow devices to cooperate rather than operate independently. Inspired by learning and decision-making processes seen in nature, the project will explore how multiple control agents can learn from experience and adapt their behaviour over time. By enabling each device to make informed decisions while considering the actions of others, the research seeks to increase energy capture, improve reliability, and support the future deployment of wave energy farms. Ultimately, the project contributes to advancing sustainable ocean energy technologies.
Applicant Eligibility
Candidates will have, or be due to obtain, a master’s degree or equivalent from a reputable university in a relevant subject OR a First in a relevant bachelor’s degree.
Important Application Process
Candidates wishing to apply should email n0mescdt@ljmu.ac.uk, to request the N0MES CDT expression of interest form to be returned with:
- degree certificates and transcripts
- an up-to-date CV
- two academic references together with their contact information
- a supporting statement [one page of A4] detailing what inspired you to apply for this project, how your skill set matches this specific project, up to 3 examples showing your commitment to science, an example of science that excites you and any further information that you think will support your application
Once your application will be assessed successful candidates will be formally invited to apply.
Please use the following as the email subject title: PhD Studentship at the N0MES CDT. Good luck!
Funding Notes
Studentships pay a maintenance grant for 4 years, starting at the UKRI minimum of £20,780 per annum for 2025-2026 and cover full home UK tuition fees (plus EU , EAA settled *see note below). The studentship also comes with access to additional funding in the form of a research training support grant which is available to fund conference attendance, fieldwork, internships etc.
Qualifications- will have, a master’s degree OR a 1st in a relevant bachelor’s degree.
References
For candidates, the following expertise/experience will be preferred, but not mandatory:
- Control theory, dynamic systems modelling and simulation
- Machine learning, data-driven modelling
- Optimisation, evolutionary computation
- Multi-agent systems, swarm intelligence
- Programming with Matlab
Candidates wishing to discuss the research project should contact the primary supervisor Dr Qian Zhang (Q.Zhang@ljmu.ac.uk).
Those wishing to discuss the application process should contact the LJMU Co-Ordinator, Dr Tasos Georgoulas A.Georgoulas@ljmu.ac.uk or n0mescdt@LJMU.ac.uk.
