PhD Studentship: Physics-Aware Machine Learning for Aerodynamics and Wind Energy

Qualification: Doctor of Philosophy in Engineering (PhD)

Award value: Home fees and tax-free stipend - See advert for details

Start date: 05 October 2026

Deadline: 29 May 2026

Project Title: Physics-Aware Machine Learning for Aerodynamics and Wind Energy

Abstract:

How can AI learn from the laws of physics to model complex systems around us? This PhD develops next-generation physics-aware machine learning methods (such as physics-informed neural networks and large-scale foundation models) and applies them to challenging problems in fluid dynamics, clean aviation, and wind energy. The project combines AI, aerodynamics, and high-fidelity simulation to enable faster, more accurate modelling for clean energy and advanced engineering systems.

Project Detail:

This fully funded PhD studentship, starting in October 2026 at the University of Warwick, offers an opportunity to work at the intersection of artificial intelligence, physics, and engineering, addressing fundamental challenges in fluid dynamics, aerodynamics, and renewable energy.

You will develop physics-aware machine learning models, such as physics-informed neural networks and physics-aware foundation models, and apply them to real-world problems, including wind flow, turbulence, and aerodynamic systems relevant to renewable energy and clean aviation.

Unlike traditional black-box machine learning, physics-aware models explicitly embed physical laws, governing equations, and domain knowledge into the learning process. This fusion enables models that are more data-efficient, interpretable, and physically consistent, even in regimes where measurements are sparse or expensive. By combining the strengths of first-principles modelling with modern AI, the research aims to deliver robust, trustworthy, and generalisable models capable of advancing scientific understanding and supporting high-impact engineering decisions.

Such advances are increasingly important for addressing complex challenges in energy, climate, and engineering systems, where reliable modelling and prediction are essential. By enabling more accurate, efficient, and trustworthy simulations, physics-aware machine learning can support the development of low-carbon technologies and system-level optimisation, contributing to global efforts in climate change mitigation.

The project is well-suited to students with backgrounds in engineering, applied mathematics, physics, computer science, or data science, and can be tailored to your interests, whether you prefer theory, modelling, or applied research.

Scholarship:

The award will cover the UK tuition fee level, plus a tax-free stipend of £21,300, paid at the prevailing UKRI rate,Link opens in a new window for 3.5 years of full-time study.

Non-UK students can apply, but will have to personally fund the difference between the Home and the Overseas rate.

Eligibility:

A degree (2:1 or above) in Mechanical Engineering, Physics, Computer Science, Applied Mathematics, or Scientific Computing. Applicants from other related disciplines are also encouraged to apply, particularly if they have a strong interest in AI and ML.

How to apply:

Candidates should submit an expression of interest by sending a CV and supporting statement outlining their skills and interests in this research area via the 'Apply' button above.

If this initial application is successful, we will invite you to submit a formal application. If invited, candidates must fulfil the University of Warwick entry criteria and obtain an unconditional offer before commencing enrolment.

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