Dimension Reduction for Digital Twin Development of Offshore Wind Turbines University of York in United Kingdom

The University of York's School of Physics, Engineering and Technology invites applications for a fully funded PhD position focused on dimension reduction for digital twin development of offshore wind turbines. Digital twins are revolutionizing the design, operation, and predictive maintenance of dynamic engineering systems, offering unprecedented accuracy and adaptability. However, their practical deployment is often hindered by the curse of dimensionality, especially in high-fidelity structural simulations involving thousands of degrees of freedom. This project aims to overcome these challenges by pioneering new mathematical and computational techniques to jointly reduce the parameter and state spaces of dynamic engineering models, enabling efficient, interpretable, and deployable digital twins for offshore wind applications.

The research aligns with international initiatives such as the IEA Wind Task 43 on Digitalisation, contributing to global efforts to improve cost efficiency, reliability, and lifetime performance of renewable energy assets. The successful candidate will develop and test a unified framework for reducing the dimensional complexity of digital twins, constructing parameter-to-state mappings that capture how uncertain inputs influence system dynamics, and applying the methodology to offshore wind turbine models, particularly in high-dimensional fatigue prediction and design optimisation tasks.

A major strength of this project is its close collaboration with OWC, part of the ABL Group—a leading global renewable energy consultancy. The candidate will be co-supervised by industrial experts from OWC, gaining access to exclusive operational datasets, advanced fatigue modelling workflows, and state-of-the-art design simulation tools. Industry placements within OWC offices in the UK and internationally will provide immersive experience, working directly alongside practising engineers and consultants on live projects. This collaboration offers unparalleled exposure to the challenges and innovations shaping digital transformation in renewable energy, and enables the candidate to translate their research into methodologies with direct industrial impact.

The PhD studentship covers the home tuition fee (£5,238 for the 2026-27 academic year), an annual stipend (£21,805 for the 2026-27 academic year) for up to 3.5 years, and a research training and support grant (RTSG). Exceptional candidates of any nationality may compete for limited full studentships covering international fees. Applicants should have a strong academic background in engineering, physics, mathematics, or a related discipline, with experience in computational modelling or mathematical techniques highly desirable. International applicants are welcome, but funding for international fees is competitive.

To apply, use the University of York's online application system and read the application guidance before starting. For technical queries, contact Dr Jiannan Yang at [email protected]. The application deadline is March 31, 2026.