PhD Studentship in Aeronautics: How offshore wind farms and clouds interact: Maximising performance with scientific machine learning (AE0078)

PhD Studentship in Aeronautics: How offshore wind farms and clouds interact: Maximising performance with scientific machine learning (AE0078)

Start: Between 1 August 2026 and 1 July 2027

The increasing size of offshore wind turbines, and wind farms, raises the question as to how they interact with the Marine Boundary Layer (MBL) which is the layer of the atmosphere immediately adjacent to the sea surface and directly influence by the sea state. This interaction is of particular interest when atmospheric conditions suit cloud formation. Windfarm operation has been shown to modify the MBL through the reduction of wind speed and promotion of turbulent kinetic energy production which affects the formation of marine stratocumulus (MS) clouds atop the MBL.

No study has yet explored the two-way interaction between wind farms and MS motivating our central research questions: “do turbine driven changes in the MBL promote or hinder MS formation?” and “how does this modified rate of MS formation affect the neighbouring mesoclimate and how does this subsequently affect the performance (i.e. power generation) of the wind farm?” This is a problem that is amenable to a scientific machine learning (SML)-based approach to identify atmospheric conditions resulting in more/less frequent MS formation, and strategies to either promote/hinder MS formation or mitigate wind-farm performance modifications due to their presence.

You will use a high-fidelity large eddy simulation (LES) code and scientific machine learning tools, such as real-time optimisers, in order to simulate wind farms exposed to various atmospheric inflows. Some small code development will be necessary to implement actuator disc/line wind-turbine models. This approach facilitates a deep understanding of the flow physics surrounding MS formation. You will develop scientific machine learning-based strategies for the discovery of self-similarity laws, use of quantised local reduced order models, and real data assimilation.

You will be assimilated, jointly, into the research groups of Prof. Oliver Buxton whose expertise is on turbulence, wind-energy flows, and turbulent cloud microphysics and Prof. Luca Magri whose expertise is in scientific machine learning for aeronautical applications, including wind energy. Both research groups currently host ERC projects and you will collaborate closely with both of these research teams.

Supervisors: Prof. Oliver Buxton; Prof. Luca Magri

Duration: 3.5 years.

Funding: Full tuition fees; annual tax-free stipend of £22,780 for Home, EU and International students.

Eligibility: Due to the competitive nature of these studentships, candidates will be expected to achieve/have achieved a First class honours MEng/MSci or higher degree (or international equivalent) in: Aeronautical/Mechanical Engineering or similar STEM subjects.

You need to be willing to learn new skills/techniques. There is some possibility for collaboration with a group in The Netherlands and so you may be required to travel for periods of a few weeks.

How to apply:

• Stage 1: Submit 2-page curriculum vitae (CV), transcripts and 300-word statement explaining your motivation for applying: Supervisor Review Form. Supervisors will perform a comprehensive review to long-list candidates.

Deadline: 8 January 2026

• Stage 2: Supervisors will email instructions and an application link to long-listed candidates, inviting them to make a formal application to the PhD Studentship.

Contact: For project questions: Prof. Oliver Buxton

For application process questions: Lisa Kelly, PhD Administrator

We are an Athena SWAN Silver Award winner, a Stonewall Diversity Champion, a Disability Confident Employer and working in partnership with GIRES to promote respect for trans people.

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