Quantitative safety analysis for integrating AI into offshore wind operations

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.

The offshore wind industry is poised for transformation through generative AI, which promises enhanced forecasting, predictive maintenance, and remote operational support but also introduces safety and human-factors challenges that remain understudied [1]. For example, machine learning (ML) models capable of real-time scenario forecasting of turbine motions can improve installation safety but require rigorous validation frameworks to manage novel failure modes. There is an urgent need for offshore wind energy organisations to systematically understand the potential risks and benefits associated with integrating generative AI, and to analyse and manage these risks effectively, transparently and collectively. The organisations are lacking quantitative frameworks to assess novel risks introduced by AI in management and operations including turbine installation and maintenance.

The proposed project will develop a probabilistic risk analysis toolkit integrating LLM-generated scenario forecasts with human-in-the-loop validation and simulations to measure and explore various factors influencing safety outcomes, including

1. Develop and visualise a “Uses+Risks+Benefits+Mitigations” dataset combining AI use scenarios, risks, benefits, and mitigations to understand and communicate risks effectively to stakeholders.

2. Develop empirical safety analysis frameworks e.g. simulation tools for risk measure and impacts on efficiency, reliability, and human performance, and evidence collection tools for timely monitoring of AI’s impact on safety, privacy, trust, equity, accessibility, and adoption outcomes.

The outcomes encompass systemic interventions including a suite of technical tools (safety analysis framework, risk dataset). This provides valuable insights to assist policymakers in systematically understanding AI risks and implementing safeguards. These contributions could inform governance strategies and regulatory frameworks for effective risk control in offshore wind industry. The generalisation of risk analysis and management methods ensures that the tools and knowledge developed can be readily adapted to safeguard other societal systems, broadening their applicability across various domains.

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 supervisor group for this project will deliver Safe AI workshops, which will provide fundamental technical aspects of the successful student’s training.

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, we would like to hear from you.

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 Zhibao Mian via z.mian2@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 Zhibao Mian via z.mian2@hull.ac.uk.

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

References

[1]D Mitchell et. al., 2022, A review: Challenges and opportunities for artificial intelligence and robotics in the offshore wind sector, Energy and AI, https://www.sciencedirect.com/science/article/pii/S2666546822000088.