Intelligent and Sustainable Offshore Wind Systems PhD

This fully funded PhD (open to applicants of any nationality), sponsored by the European Union Horizon Europe programme, offers an exciting opportunity to develop next-generation digital twin technologies for sustainable offshore wind systems. The research will combine digital twins, artificial intelligence, predictive maintenance, life cycle assessment and techno-economic analysis to improve the reliability, sustainability and long-term performance of offshore wind assets. Working within a large international consortium of leading universities and industrial partners, the successful candidate will contribute to cutting-edge research supporting the digital transformation and decarbonisation of offshore renewable energy. Offshore wind is playing an increasingly important role in achieving global net-zero targets. As offshore wind farms become larger and more complex, improving operational efficiency while reducing environmental impacts and lifecycle costs has become a major challenge. Advanced sensors, artificial intelligence and digital engineering are transforming the way offshore assets are operated, enabling predictive maintenance, sustainability assessment and data-driven decision-making. This project sits at the intersection of offshore engineering, digital engineering, sustainability and artificial intelligence. The PhD will develop next-generation digital twin methodologies for offshore wind systems by integrating real-time monitoring data, digital engineering, predictive maintenance and sustainability assessment. The research will investigate data integration, digital twin development, lifecycle assessment, techno-economic analysis and intelligent maintenance strategies to quantify the engineering, environmental and economic benefits of digital technologies. Computational modelling, digital twin development and experimental validation will be combined to support more sustainable and resilient offshore wind systems. The project is hosted by Cranfield University, one of the UK’s leading universities for postgraduate engineering research. It is funded by the European Union Horizon Europe programme, and forms part of a large international research programme, involving leading universities, research institutes and industrial partners across Europe. The successful candidate will collaborate with experts in offshore engineering, digital technologies and sustainability, contributing to a multidisciplinary research programme addressing key challenges in offshore renewable energy. The research will demonstrate how digital twin technologies can improve the sustainability, reliability and reduce lifetime costs of offshore wind systems throughout their operational lifecycle. The project will deliver methodologies for evaluating lifecycle environmental impacts, techno-economic performance and maintenance strategies, supporting the wider deployment of intelligent offshore renewable energy technologies and contributing to global decarbonisation objectives. The student will become part of a major Horizon Europe collaborative project involving leading universities and industrial partners across Europe. Opportunities will include publishing in leading international journals, travel opportunities such as presenting research at international conferences and participating in collaborative meetings, and to develop multidisciplinary expertise in offshore engineering, AI and digital twins, and to gain other experiences in an international flagship project. Access to Cranfield’s cutting-edge specialist laboratories and research facilities will support both computational and experimental research.

Applicants should have a first or second class UK honours degree or equivalent in a related discipline. This project would suit graduates in Mechanical Engineering, Civil Engineering, Environmental Engineering, Sustainability, Energy Systems, Renewable Energy, Electrical Engineering, Computer Science, Artificial Intelligence, Data Science, or other relevant STEM disciplines. Experience in one of more of offshore systems, sustainability assessment, programming, data analysis or AI modelling would be advantageous but is not essential. Applicants should have strong analytical and problem-solving skills, together with an interest in digital twins, AI and offshore renewable energy, and the willingness to collaborate with other partners to contribute to a multidisciplinary EU project.