Available Projects
Physics Meets AI: Scientific Machine Learning for Offshore Wind Farm Modelling
This PhD will develop physics-aware AI models tailored for offshore wind applications. By embedding physical laws into machine learning (ML), the research aims to deliver fast, reliable digital models. The work will contribute to improved wind-farm design, enhanced reliability, and reduced costs. The student will develop strong expertise in physics-informed machine learning, renewable energy, and digital twins—skills that are increasingly important across scientific and engineering disciplines.
Primary supervisor: Dr Jincheng Zhang - Email: Jincheng.Zhang.1@warwick.ac.uk
Co-supervisor: Peter Brommer
Project detail:
This project investigates how physics-aware machine learning can be used to model offshore wind farms more effectively than traditional black-box approaches. The student will build new ML frameworks that incorporate physical invariances, multi-fidelity data, and uncertainty quantification. The resulting models will serve as fast, physically grounded surrogates suitable for digital-twin applications and large-scale engineering analysis.
The research outcomes are expected to have both academic and industrial impact, including high-quality publications and open-source software demonstrating best practices in scientific AI and wind-energy modelling. The student will gain broad expertise spanning wind energy, multi-fidelity modelling, surrogate modelling, and physics-informed ML.
Supervision and Research Environment
The project is supported by a multidisciplinary supervisory team:
• Primary Supervisor: Dr Jincheng Zhang
• Co-Supervisor: Dr Peter Brommer
• Industry Co-Supervisor: Dr David Standingford
This team provides a strong environment combining expertise in physics-informed AI, computational modelling, and renewable energy applications, with additional industrial insight to ensure the research remains relevant to real-world challenges.
If you are interested in this PhD opportunity, please send your CV and a cover letter outlining your skills and research interests to: jincheng.zhang.1@warwick.ac.uk. Further information on related research topics can be found at: https://zhangxcii.github.io.
How to apply for admission: www.warwick.ac.uk/pgrengineering
How to apply for a scholarship: https://warwick.ac.uk/fac/sci/eng/postgraduate/funding/
The University of Warwick provides an inclusive working and learning environment, recognising and respecting every individual’s differences. We welcome applications from individuals who identify with any of the protected characteristics defined by the Equality Act 2010.