Explainable Predictive AI Models for Environmental Impact of Offshore Wind Farms

Project Description:

This PhD scholarship is offered by the EPSRC CDT in Offshore Wind Energy Sustainability and Resilience; a partnership between the Universities of Durham, Hull, Loughborough and Sheffield. The successful applicant will undertake six-month of training with the rest of the CDT cohort at the University of Hull before continuing their PhD research at Hull.

Offshore wind is vital to the UK’s transition to net zero, but as the industry scales, understanding and managing its environmental and socio-economic impacts becomes increasingly important. This PhD will develop explainable predictive AI models to assess and forecast how offshore wind farms influence marine ecosystems, seabed mobility, and local industries such as fishing.

Unlike traditional “black-box” AI, these models will focus on explainability, ensuring that predictions are transparent and trustworthy for regulators, developers, and local communities. The research will integrate diverse datasets from ecological monitoring, geospatial surveys, and socio-economic sources (including DEFRA and MMO datasets) to build models that capture both environmental and human dimensions of offshore wind.

By combining machine learning with physics-informed modelling, the project will deliver predictive tools that remain consistent with scientific principles while being interpretable to non-specialists. This will allow stakeholders to better anticipate biodiversity changes, manage seabed risks, and understand socio-economic trade-offs.

The student will join a dynamic research environment at Hull and Loughborough Universities, working closely with other PhDs in the cluster on sustainable offshore wind. They will also engage with industry partners and policymakers, ensuring that research outputs have direct real-world impact.

Training and development

You will benefit from a taught programme, giving you a broad understanding of the breadth and depth of current and emerging offshore wind sector needs. This begins with an intensive six-month programme at the University of Hull for the new student intake, drawing on the expertise and facilities of all four academic partners. It is supplemented by Continuing Professional Development (CPD), which is embedded throughout your 4-year research scholarship.

The successful candidate will gain cutting-edge expertise in AI, sustainability, and stakeholder engagement—skills that are in high demand in both academia and industry.

Entry requirements

If you have received a First-class Honours degree, or a 2:1 Honours degree and a Masters, or a Distinction at Masters level with any undergraduate degree (or the international equivalents) in Computer Science, Data Science, Mathematics and Statistics, or related quantitative disciplines, with strong skills in programming and machine learning, we would like to hear from you. Experience or interest in environmental science and sustainability will be highly advantageous.

If your first language is not English, or you require Tier 4 student visa to study, you will be required to provide evidence of your English language proficiency level that meets the requirements of the Aura CDT’s academic partners. This course requires academic IELTS 7.0 overall, with no less than 6.0 in each skill.

If you have any queries about this project, please contact Dr Xinhui Ma, xinhui.ma@hull.ac.uk 

You may also address queries about the CDT to auracdt@hull.ac.uk.

Watch our short video to hear from Aura CDT students, academics and industry partners:

Funding

The Offshore Wind CDT is funded by the EPSRC, allowing us to provide scholarships that cover fees plus a stipend set at the UKRI nationally agreed rates. These are currently £20,780 per annum at 2025/26 rates and will increase in line with the EPSRC guidelines for the subsequent years (subject to progress).

Eligibility

Research Council funding for postgraduate research has residence requirements. Our CDT scholarships are available to Home (UK) Students. To be considered a Home student, and therefore eligible for a full award, a student must have no restrictions on how long they can stay in the UK and have been ordinarily resident in the UK for at least 3 years prior to the start of the scholarship (with some further constraint regarding residence for education). For full eligibility information, please refer to the EPSRC website.

We also allocate a number of scholarships for International Students per cohort.

Guaranteed Interview Scheme

The CDT is committed to generating a diverse and inclusive training programme and is looking to attract applicants from all backgrounds. We offer a Guaranteed Interview Scheme for home fee status candidates who identify as Black or Black mixed or Asian or Asian mixed if they meet the programme entry requirements. This positive action is to support recruitment of these under-represented ethnic groups to our programme and is an opt in process.

Find out more

How to apply

Applications for this project will open in Autumn 2025 for September 2026 entry.

Interviews will be held online with an interview panel comprising of project supervisory team members from the host university where the project is based.  Where the project involves external supervisors from university partners or industry sponsors then representatives from these partners may form part of the interview panel and your supplementary application form will be shared with them (with the guaranteed interview scheme section removed).

If you have any queries about this project, please contact Dr Xinhui Ma, xinhui.ma@hull.ac.uk 

You may also address queries about the CDT to auracdt@hull.ac.uk.

References & Further Reading 

Díaz, H., Guedes Soares, C., & Bhattacharjee, J. (2021). Machine learning techniques for environmental impact assessment: A systematic review. Environmental Modelling & Software, 145, 105188. 

Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2018). A survey of methods for explaining black box models. ACM Computing Surveys, 51(5), 93. 

Nnajiofor, C.A., Eyo, D.E., Adegbite, A.O., Abdullahi, I., Odoguje, E.W.S., Folorunsho, F.E. and Adeyeye, A.A., 2024. Leveraging Artificial Intelligence for optimizing renewable energy systems: A pathway to environmental sustainability. environment, 24, p.25. 

Astolfi, D., De Caro, F. and Vaccaro, A., 2023. Condition monitoring of wind turbine systems by explainable artificial intelligence techniques. Sensors, 23(12), p.5376.