Stephen James

Two fully funded PhD studentships are available at Cranfield University in the broad area of offshore wind , digital twins , artificial intelligence , structural health monitoring , predictive maintenance , life cycle assessment , and techno-economic analysis . The projects are embedded in an EU Horizon Europe research programme and involve collaboration with leading universities, research institutes, and industrial partners across Europe. Project 1: Digital Twins for Offshore Wind Systems focuses on physics-informed digital twins for offshore wind turbines. The research combines real-time monitoring data, advanced physics modelling, AI-assisted analytics, structural state reconstruction, fatigue assessment, remaining useful life prediction, and predictive maintenance. Laboratory experiments and validation are part of the work. Project 2: Intelligent and Sustainable Offshore Wind Systems develops next-generation digital twin methodologies for sustainable offshore wind systems. It integrates digital engineering, AI, predictive maintenance, life cycle assessment, and techno-economic analysis to quantify engineering, environmental, and economic benefits and to support more resilient and sustainable offshore wind operations. Funding includes a tax-free bursary of up to £28,830 per year for 3 years plus full tuition fees , including international fees. The studentships also provide access to specialist laboratories, computational resources, conference support, international collaboration opportunities, and consumables. Eligibility : applicants should hold a first or second class UK honours degree or equivalent in a relevant discipline. Suitable backgrounds include Mechanical Engineering, Civil Engineering, Ocean/Offshore Engineering, Renewable Energy, Structural Engineering, Electrical Engineering, Computer Science, Artificial Intelligence, Data Science, Environmental Engineering, Sustainability, or related STEM fields. Experience with offshore systems, sensors, experiments, programming, data analysis, AI modelling, or sustainability assessment is helpful but not required. Deadline : 26 August 2026. The start date is 2 November 2026. Early application is encouraged because the vacancy may be filled before the closing date. How to apply : submit the online application form and upload all required documents. Informal enquiries can be directed to Dr Luofeng Huang at [email protected] .