Digital Twins for Offshore Wind Systems PhD

This fully funded PhD (open to any nationality), sponsored by the European Union Horizon Europe programme, offers an exciting opportunity to develop next-generation digital twins for offshore wind turbines. The research will combine physics-based modelling, artificial intelligence, structural health monitoring and real-time data integration to improve asset reliability and predictive maintenance. Working within an international consortium of leading universities and industrial partners, the successful candidate will contribute to cutting-edge research that supports the digital transformation and decarbonisation of the offshore renewable energy sector. Offshore wind is a cornerstone of the global transition to net-zero energy. As wind farms become larger and move further offshore, ensuring their structural reliability while reducing maintenance costs has become a major engineering challenge. Digital twins, artificial intelligence and structural health monitoring are transforming the way offshore assets are monitored and maintained, enabling safer, smarter and more sustainable renewable energy systems. This project sits at the intersection of offshore engineering, digital engineering and AI The PhD will develop physics-informed digital twins for offshore wind turbines by integrating real-time monitoring data, advanced physics modelling and AI-assisted data analytics. The research will investigate data integration, structural state reconstruction, fatigue assessment, remaining useful life prediction and predictive maintenance. Digital twin development and validation using laboratory experiments will be undertaken to improve the reliability and operational performance of offshore wind assets. 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 work within a multidisciplinary team with access to state-of-the-art laboratories and international expertise. The research will contribute to the development of next-generation digital twin technologies for offshore wind systems, enabling improved structural integrity assessment, predictive maintenance and lifecycle management. The outcomes are expected to reduce maintenance costs, improve operational reliability and support the wider deployment of offshore renewable energy, contributing to global decarbonisation and energy security. The student will become part of a major Horizon Europe collaborative project and have opportunities to work with international academic and industrial partners across Europe. The project offers opportunities to publish in leading 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. The student will develop advanced skills in digital twins, AI, physics modelling, structural health monitoring, data analytics and scientific programming, together with experience in laboratory testing, project management and international collaboration. These highly transferable skills will provide excellent career opportunities in academia, offshore renewable energy, digital engineering, consulting, and the wider energy and technology sectors.

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, Ocean Engineering, Offshore Engineering, Renewable Energy, Structural Engineering, Electrical Engineering, Computer Science, Artificial Intelligence, Data Science, or other relevant STEM disciplines. Experience in one or more of offshore systems, advanced sensors, experiments, 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