Machine Learning in environmental fluid dynamics modelling

About the project

Join an exciting PhD at the University of Southampton, where you will harness cutting-edge Physics-Informed Neural Networks to transform environmental flow modelling. Combining fluid mechanics, machine learning and world-class supercomputing, you will develop innovative solutions to real-world challenges, from flood simulations to sustainable engineering design, within a collaborative, internationally renowned research community.

Are you passionate about leveraging artificial intelligence to address pressing environmental challenges? Join our innovative team at the University of Southampton as we explore groundbreaking applications of Physics-Informed Neural Networks (PINNs) in solving complex environmental flow problems.

This exciting PhD project focuses on using advanced machine-learning techniques to simulate environmental flows, including the 2D Shallow Water Equations, pivotal for understanding flood dynamics, to guide engineering design of submerged infrastructure, and in water resource management.

The project builds on our latest research in PINNs, merging fluid mechanics with state-of-the-art AI to achieve faster, more accurate, and robust solutions for real-world environmental challenges.

As part of this research, you will join a vibrant community of PhD students working on cutting-edge tools at the intersection of machine learning and fluid dynamics. Our Water and Environmental Research Group is a world leader in computational modelling and environmental science, providing an inspiring and collaborative environment to help you excel in your studies.

In addition, you will have access to one of the fastest supercomputing facilities in the UK, enabling high-performance simulations and data analysis to push the boundaries of current environmental flow modelling techniques.

We welcome applications from talented students around the world and encourage individuals from diverse backgrounds to apply. Funding is awarded competitively based on academic excellence.

What we offer:

Embark on a journey where machine learning meets environmental engineering, and contribute to a sustainable future with AI-driven solutions. Apply now to make your mark in this transformative field!

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.

• a dynamic and supportive research community at a top UK university
• access to cutting-edge computational resources to accelerate your research.

Lead supervisor

• Lead supervisor: GDA Dr Gustavo De Almeida Associate Professor

Dr Gustavo De Almeida

• Dr Gustavo De Almeida: Associate Professor

Dr Sergio Maldonado PhD, MSc, BSc (Eng)

• 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:

A Master's degree in a relevant subject is desirable.

Essential skills and experience:

Desirable skills:

• physics
• engineering
• applied mathematics
• or a closely related discipline
• strong academic background in fluid mechanics
• passion for interdisciplinary research combining AI and fluid dynamics
• programming experience
• familiarity with numerical methods

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

Apply now

You need to:

Applications should include:

• choose programme type (Research), 2027/28, 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
• your CV (resumé)
• 2 academic references
• degree transcripts and certificates to date
• English language qualification (if applicable)

Faculty of Engineering and Physical Sciences

For questions about applying, email our Doctoral College (doctoralcollege-admissions@soton.ac.uk) .

Project leader

For an initial conversation, email Dr Gustavo de Almeida (g.dealmeida@soton.ac.uk).

Research areas