Supervisory Team: Gustavo de Almeida and Dr Sergio Maldonado

This PhD project explores the use of Physics-Informed Neural Networks (PINNs) to solve environmental flow problems, including the 2D Shallow Water Equations. Combining advanced AI with fluid mechanics, the research aims to develop fast, accurate, and robust simulations for applications like flood modeling and water management. As part of a leading research group, the candidate will have access to world-class expertise and one of the UK’s fastest supercomputing facilities to drive innovation in environmental science.

This PhD project investigates the development of Physics-Informed Neural Networks (PINNs) to solve challenging environmental flow problems, such as the 2D Shallow Water Equations. PINNs combine data-driven machine-learning techniques with the governing physics of fluid systems to create fast, accurate, and computationally efficient models. This innovative approach has the potential to revolutionize how environmental flows are simulated, with applications in flood prediction, engineering design and water resource management.

Building on recent advancements in PINNs and their integration into fluid mechanics, this project will develop state-of-the-art methodologies for modeling complex environmental systems. The successful candidate will explore and refine these techniques, pushing the boundaries of their application to real-world challenges.

The PhD is hosted by the Water and Environmental Research Group at the University of Southampton, a leader in environmental and computational modeling. The candidate will join a dynamic cohort of researchers working on cutting-edge machine-learning tools for fluid dynamics, fostering collaboration and innovation in this rapidly growing field.

To support this work, the successful applicant will have access to one of the UK’s fastest supercomputing facilities, enabling high-performance simulations and advanced computational analysis.

We are seeking highly motivated candidates with a strong background in Fluid Mechanics and a degree in Physics, Engineering, or Applied Mathematics. Prior experience with programming and numerical methods is advantageous.

This project offers an excellent opportunity to contribute to impactful research at the intersection of artificial intelligence and environmental science, shaping solutions for critical global challenges.

Entry Requirements

We are looking for highly motivated applicants with strong background and interests in fluid mechanics and machine learning. Applicants must have a strong undergraduate or masters degree in engineering, physics or applied mathematics.

Closing date : (27 June 2025). Applications will be considered in the order that they are received, the position will be considered filled when a suitable candidate has been identified.

Funding: We offer a range of funding opportunities for both UK and international students, including Bursaries and Scholarships. For more information please visit PhD Scholarships | Doctoral College | University of Southampton Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.

How To Apply

Apply online: Search for a Postgraduate Programme of Study (soton.ac.uk) Select programme type (Research), 2025/26, Faculty of Engineering and Physical Sciences, next page select “PhD (Eng & Env)”. In Section 2 of the application form you should insert the name of the supervisor

Applications should include :

Research Proposal

Curriculum Vitae

Two reference letters

Degree Transcripts/Certificates to date