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Jiannan Yang
1 year ago
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PhD in Dimension Reduction for Digital Twin Development of Offshore Wind Turbines University of York in United Kingdom
Degree Level
PhD
Field of study
Systems Engineering
Funding
Funded PhD Project (Students Worldwide)
Deadline
Expired
Country
United Kingdom
University
University of York
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Keywords
About this position
This PhD project at the University of York focuses on developing advanced dimension-reduction techniques for digital twin (DT) models in the offshore wind industry. Digital twins are revolutionizing the design, operation, and predictive maintenance of dynamic engineering systems, offering enhanced accuracy and adaptability. However, their practical deployment is hindered by the 'curse of dimensionality,' as high-fidelity simulations of turbine blades and structures involve thousands of degrees of freedom, making real-time applications computationally intensive.
The successful candidate will pioneer new mathematical and computational methods to jointly reduce both the parameter and state spaces of dynamic engineering models, enabling efficient, interpretable, and deployable digital twins for offshore wind applications. The research aims to construct parameter-to-state mappings that capture the influence of uncertain inputs on system dynamics, develop a unified framework for joint dimension reduction, and validate these methods on offshore wind turbine models, particularly for high-dimensional fatigue prediction and design optimization tasks. The project is closely aligned with international initiatives such as the IEA Wind Task 43 on Digitalisation, contributing to global efforts to enhance the cost efficiency, reliability, and longevity of renewable energy assets. A key feature of this PhD is its strong industrial collaboration with OWC, part of the ABL Group, a leading global renewable energy consultancy.
The candidate will be co-supervised by industrial experts, gain access to exclusive operational datasets, advanced fatigue modelling workflows, and state-of-the-art simulation tools, and undertake industry placements in the UK and internationally. This collaboration provides unique exposure to real-world challenges and innovations in digital transformation for renewable energy, allowing the candidate to translate research into impactful industrial methodologies. The position is based in the School of Physics, Engineering and Technology at the University of York.
Funding covers home tuition fees, an annual stipend, and a research training and support grant, with limited full studentships available for exceptional international candidates. Applicants should have a strong background in engineering, applied mathematics, physics, or a related field, with excellent mathematical and computational skills.
The application deadline is January 31, 2026, for a September 2026 start.
Funding details
Funded PhD Project (Students Worldwide)
What's required
Applicants should hold, or expect to obtain, a good honours degree (typically a 2:1 or above) or a master's degree in engineering, applied mathematics, physics, or a related discipline. Strong mathematical and computational skills are essential. Experience in structural mechanics, dynamic systems, or computational modelling is desirable. International applicants may need to provide evidence of English language proficiency (such as IELTS or equivalent) as required by the University of York.
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Professors
Dr at School of Physics, Engineering and Technology
United Kingdom
Research Interests
Energy Engineering
Structural Engineering
Optimisation
Digital Twins
Dimensionality Reduction
Make your next move.
Prepare your application
Review drafts and confirm requirements with the university.