Artificial intelligence for environmental fluid dynamics modelling

About the Project

Supervisory team: Dr Gustavo de Almeida and Dr Sergio Maldonado

This exciting PhD project explores the use of Physics-Informed Neural Networks (PINNs) to model complex environmental flows. By integrating AI with fluid mechanics, it aims to enhance simulations of the 2D Shallow Water Equations for applications in flood prediction, infrastructure design, and sustainable water management.

The PhD will focus on developing advanced machine-learning models—specifically Physics-Informed Neural Networks (PINNs)—to simulate environmental flows governed by the 2D Shallow Water Equations. These equations are fundamental to understanding flood dynamics, designing submerged infrastructure, and managing water resources effectively.

By merging machine-learning methods with the physical principles of fluid mechanics, the project aims to deliver faster, more accurate, and robust solutions to real-world environmental challenges.

You will become part of a vibrant, interdisciplinary research community within the University of Southampton’s Water and Environmental Engineering Research Group, a global leader in computational modelling and environmental science.

The project offers opportunities to collaborate with experts in AI, physics, and engineering, benefiting from a highly supportive and innovative environment. Access to one of the UK’s fastest supercomputing facilities will enable high-performance simulations and large-scale data analysis, allowing the candidate to push the boundaries of environmental flow modelling.

You will benefit from comprehensive training across engineering, applied mathematics, and computer science, with access to advanced modules in topics such as fluid dynamics, numerical modelling, and machine learning.

You will receive close supervision and mentorship from an outstanding interdisciplinary team of experts, ensuring strong technical development and research excellence throughout your PhD.

We welcome applicants from around the world and encourage diversity in research. Join us to pioneer AI-driven approaches to environmental engineering.

Entry requirements:

You must have a UK 2:1 honours degree, or its international equivalent, in one of the following:

• engineering
• physics
• applied mathematics

You must have:

• a strong background and interests in fluid mechanics and machine learning
• enthusiasm for integrating AI into environmental research

Programming experience and familiarity with numerical methods are highly desirable.

Funded studentships are available for outstanding candidates on a competitive basis.

Fees and funding:

Full scholarships include tuition fees, a tax-free stipend at the UKRI rate for up to 3.5 years (totalling £20,780 for 2025/26, rising annually). UK, EU and Horizon Europe students are eligible for scholarships. Chinese Scholarship Council funded students are eligible for fee waivers. Funding for other international applicants is very limited and highly competitive. Overseas students who have secured or are seeking external funding are welcome to apply. For more information, please visit our postgraduate research funding pages.

How to apply:

Apply now

You need to:

• choose programme type (Research), 2026/27, Faculty of Engineering and Physical Sciences
• select Full time or Part time
• search for programme PhD Engineering & the Environment (7175)
• add name of the supervisor in section 2 of the application

Applications should include:

• your CV (resumé)
• 2 academic references
• degree transcripts and certificates to date
• English language qualification (if applicable)