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 are open until 5 January 2026.

Please note, you may only apply for ONE project offered through the EPSRC CDT in Offshore Wind Energy Sustainability and Resilience.

Please ensure that you familiarise yourself with the Aura CDT website before you apply to give you a good understanding of what a CDT is, our CDT’s research focus and the training and continuing professional development programme that runs alongside the CDT. The Frequently asked questions page and Candidate resources page are essential reading prior to applying.

Applications to this project are made via the University of Hull admissions system. If you have not applied to the University of Hull before, you will need to set up an account to enable you to track the progress of your application and upload supporting documents.

As part of the recruitment process, we ask that you submit a short film of you delivering a presentation, of up to 5 minutes in length, on “How do your experiences and qualities provide a background to contribute to research and innovation for the project you have applied for”.

You will be assessed on the content of your presentation, not your film editing skills, but please be mindful of filming in an appropriate, quiet location. Please film the presentation in whatever way you feel most comfortable with. For example, it could be a slide presentation with voice over, or you may wish to present simply talking to the camera.  Please use the tools and technology that are accessible to you and that you feel comfortable with e.g. your mobile phone, or the built-in ‘Record Slide Show’ on Keynote (macOS, iOS, iPadOS) or Powerpoint etc.

We also ask that you complete a Supplementary Application Form. This includes space for you to provide a link where the shortlisting panel may view your film.

Follow the relevant link to apply for this CDT project at the University of Hull:

Apply for a Full-time place (4-year programme)

Apply for a Part-time place (8-year programme, available for Home students only due to visa restrictions)

With your application, you need to upload copies of the following supporting evidence:

• Complete transcripts (and final degree certificate(s) where possible). If your qualification documents are not in English, you will need to supply copies of your original language documents as well as their official translation into English.
• Your Curriculum Vitae (CV).
• A completed Supplementary Application Form (upload when asked to add a Research proposal).

Uploading the form 

When you have completed the form, please save it as a pdf format and labelled as follows:

Last name_first name PhD application form

Upload the form as part of your application documents through the University of Hull student application portal, when asked to add a Research Proposal. The Form replaces the Research Proposal and so you do not need to add a Research Proposal. Please do not send your form directly to the Offshore Wind CDT.

Interviews

First-round interviews will be held online during early to mid-February 2026. The interview panel will comprise the project supervisory team members from the host university where the project is based, plus a representative of the CDT.  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 application documents will be shared with them (with the guaranteed interview scheme section of the supplementary application form removed).

If you are successful, you will progress to a second interview towards the end of February 2026. This will be with key academics from the CDT from across our four partner institutions (Durham University, University of Hull, Loughborough University, University of Sheffield) and your application documents will be shared with them (with the guaranteed interview scheme section removed from the supplementary application form).

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.