[£20,780 per annum. EPSRC CDT studentship covers stipend and likely tuition for 4 years.] This fully funded PhD studentship at the University of Hull, in partnership with the EPSRC Centre for Doctoral Training (CDT) in Offshore Wind Energy Sustainability and Resilience, offers an exciting opportunity to develop explainable predictive AI models for assessing the environmental impact of offshore wind farms. The project is supervised by Dr Xinhui Ma and Dr Koorosh Aslansefat (University of Hull), and Prof Nina Dethlefs (Loughborough University), and is part of a collaboration between the Universities of Durham, Hull, Loughborough, and Sheffield. Offshore wind energy is a cornerstone of the UK's net zero strategy, but its rapid expansion necessitates a deeper understanding of its environmental and socio-economic effects. This PhD will focus on creating transparent, explainable AI models to predict how offshore wind farms affect marine ecosystems, seabed mobility, and local industries such as fishing. 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 development. Unlike traditional 'black-box' AI, the models developed in this project will prioritize explainability, ensuring that predictions are accessible and trustworthy for regulators, developers, and local communities. By combining machine learning with physics-informed modeling, the project aims to deliver predictive tools that are both scientifically robust and interpretable to non-specialists. These tools will help stakeholders anticipate biodiversity changes, manage seabed risks, and understand socio-economic trade-offs associated with offshore wind projects. The successful candidate will join a vibrant research environment at Hull and Loughborough, collaborating with other PhD students in the sustainable offshore wind cluster and engaging with industry partners and policymakers. The program includes an intensive six-month training period at the University of Hull, followed by ongoing professional development throughout the four-year scholarship. Students will gain expertise in AI, sustainability, and stakeholder engagement—skills highly valued in both academia and industry. Funding: The studentship provides a stipend of £20,780 per annum, with tuition fees covered for four years. Eligibility: Applicants should hold 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 international equivalents) in Computer Science, Data Science, Mathematics and Statistics, or related quantitative disciplines. Strong programming and machine learning skills are essential. Experience or interest in environmental science and sustainability is highly desirable. Non-native English speakers or those requiring a Tier 4 visa must provide evidence of English proficiency (IELTS 7.0 overall, no less than 6.0 in each skill). Application deadline: 5 January 2025. For more information and to apply, visit the project link or contact Dr Xinhui Ma at [email protected].

[£20,780 per annum stipend for 4 years, plus training and development opportunities through the EPSRC CDT partnership.] This fully funded PhD studentship at the University of Hull, in partnership with the EPSRC Centre for Doctoral Training (CDT) in Offshore Wind Energy Sustainability and Resilience, offers a unique opportunity to advance research at the intersection of artificial intelligence and environmental science. The project focuses on developing explainable predictive AI models to assess and forecast the environmental and socio-economic impacts of offshore wind farms. As offshore wind energy expands to support the UK's net zero ambitions, understanding its effects on marine ecosystems, seabed mobility, and local industries such as fishing is increasingly critical. Unlike traditional black-box AI approaches, this research emphasizes model explainability, ensuring that predictions are transparent and trustworthy for regulators, developers, and local communities. The successful candidate 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 modeling, the project aims to deliver predictive tools that are scientifically robust and interpretable to non-specialists, enabling stakeholders to anticipate biodiversity changes, manage seabed risks, and understand socio-economic trade-offs. The student will join a vibrant research environment at Hull and Loughborough Universities, collaborating with other PhDs in the cluster focused on sustainable offshore wind. Engagement with industry partners and policymakers will ensure that research outputs have direct real-world impact. The studentship includes an intensive six-month training programme at the University of Hull, followed by continued research and professional development throughout the four-year scholarship. Training covers current and emerging needs in the offshore wind sector, supplemented by Continuing Professional Development (CPD) opportunities. Eligibility requirements include 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 international equivalents) in Computer Science, Data Science, Mathematics and Statistics, or related quantitative disciplines. Strong programming and machine learning skills are essential, and experience or interest in environmental science and sustainability is highly advantageous. Applicants whose first language is not English, or who require a Tier 4 student visa, must provide evidence of English language proficiency (IELTS 7.0 overall, with no less than 6.0 in each skill). The studentship provides a stipend of £20,780 per annum for four years, along with access to training and development opportunities through the EPSRC CDT partnership. The application deadline is November 30, 2026. For further information or enquiries, contact Dr Xinhui Ma at [email protected]. Apply via the Aura CDT website, ensuring your application highlights relevant skills and experience in AI, programming, and environmental science.