About the project
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.
The School of Engineering is committed to promoting equality, diversity inclusivity as demonstrated by our Athena SWAN award. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break. The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance. The University of Southampton is committed to sustainability and has been awarded the Platinum EcoAward.
Potential supervisors
Lead supervisor
GDA
Dr Gustavo De Almeida
Supervisors
Dr Sergio Maldonado PhD, MSc, BSc (Eng)
Lecturer B
Research interests
- Environmental fluid mechanics and hydraulics
- Dynamics of various fluid-particle (algae, plastics, etc.) interactions
- Use of AI (Physics Informed Neural Networks) in fluid dynamics
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.Â
Fees and funding
We offer a range of funding opportunities for both UK and international students. Horizon Europe fee waivers automatically cover the difference between overseas and UK fees for qualifying students.
Competition-based Presidential Bursaries from the University cover the difference between overseas and UK fees for top-ranked applicants.
Competition-based studentships offered by our schools typically cover UK-level tuition fees and a stipend for living costs for top-ranked applicants.
Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.
For more information, please visit our postgraduate research funding pages.
How to apply
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)
Contact us
Faculty of Engineering and Physical Sciences
If you have a general question, email our doctoral college (feps-pgr-apply@soton.ac.uk).
Project leader
For an initial conversation, email Dr Gustavo De Almeida (G.deAlmeida@soton.ac.uk).
