PhD Studentship – Physics-Informed Neural Surrogates for Real-Time Digital Twins and CFD Visualisation of Offshore Wind Turbines
University of Plymouth
Plymouth, United Kingdom
The University of Plymouth is offering a fully funded PhD studentship focused on the development of physics-informed neural surrogate models for real-time digital twins and computational fluid dynamics (CFD) visualisation of offshore wind turbines. This interdisciplinary research project lies at the intersection of artificial intelligence, fluid dynamics, and renewable energy systems, aiming to advance next-generation modelling and simulation tools for offshore wind applications.
The project will explore the integration of physics-informed neural networks (PINNs) with CFD techniques to enable rapid, high-fidelity simulations and real-time monitoring of offshore wind turbine performance. The successful candidate will work on developing surrogate models capable of accelerating simulations while maintaining physical accuracy, contributing to improved turbine design, optimisation, and operational efficiency. The research will also involve visualisation techniques for complex flow dynamics and digital twin frameworks to support predictive maintenance and decision-making.
The PhD candidate will be based within a supportive research environment at the University of Plymouth, collaborating with academic supervisors and potentially industry partners. The project offers access to advanced computational resources and training in both theoretical and applied aspects of machine learning and fluid mechanics.
Eligibility Criteria
Applicants should hold, or expect to obtain, a first-class or upper second-class honours degree (or equivalent) in a relevant discipline such as mechanical engineering, aerospace engineering, applied mathematics, physics, computer science, or a related field. A master’s degree in a relevant subject is desirable but not essential. Candidates must meet the University of Plymouth’s postgraduate research entry requirements, including English language proficiency where applicable.
Required expertise/skills
A strong background in mathematics, fluid dynamics, or computational methods is essential. Experience or familiarity with machine learning, neural networks, or data-driven modelling approaches is highly desirable. Candidates should demonstrate programming skills (e.g., Python, MATLAB, or similar), analytical thinking, and problem-solving abilities. An interest in renewable energy systems and offshore wind technologies is advantageous. Strong communication skills and the ability to work both independently and collaboratively are required.
Salary details
The studentship includes full tuition fee coverage (Home rate) and a tax-free stipend aligned with UKRI rates for the duration of the project. Additional support for research training and conference attendance may be available.
Application Deadline
Not specified