AI-enhanced modelling of liquid hydrogen flows for net-zero transportation

This exciting opportunity is based within the Mechanical and Aerospace Systems research group in the Faculty of Engineering which conducts cutting edge research into thermofluids in applied fields such as fuel systems, transportation and power generation.

Vision

We are seeking a highly motivated PhD researcher with a passion for fluid dynamics, AI, and sustainable aviation. The vision of this PhD is to create the next generation of modelling tools for liquid hydrogen (LH₂) fuel systems—a critical requirement for future hydrogen-powered aircraft concepts. This opportunity will drive advances in cryogenic modelling, two-phase CFD, and AI-based reduced-order models to accelerate modelling capability in net‑zero aerospace technologies.

Motivation 

Hydrogen research has accelerated to address the need for a carbon neutral fuel across a broad range of industries. The transport sector has identified liquid hydrogen as a suitable fuel source for hydrogen combustion engines and hydrogen fuel cells, such as Airbus’ ZEROe concepts. However, liquid hydrogen fuel systems remain largely unstudied and critical fundamental research and modelling capability needs to be developed to strengthen the necessary engineering excellence needed for the aerospace sector. In this PhD, high-fidelity two-phase Computational Fluid Dynamics (CFD) methods will be used to model complex and fundamental cryogenic hydrogen flows for fuel system applications. While these methods provide a wealth of knowledge and information, they remain impractical for industrial use. Therefore, AI modelling techniques will be harnessed to develop practical models for the aerospace industry.

Aim

During this PhD you will develop state-of-the-art high-fidelity cryogenic CFD models, generate high‑resolution datasets, and train AI models to reveal underlying physics while enabling real‑time or near‑real‑time predictions. 

You will work with experts in engineering, CFD, data-driven fluid dynamics and computer science. This PhD provides an excellent platform for careers in academia, aerospace R&D, or sustainable propulsion.

Candidate requirements

We are looking for an enthusiastic, self-motivated researcher with strong analytical skills, an interest in CFD, thermofluids and machine learning. 

Experience in Python - or another language -, machine learning frameworks, or CFD tools such as OpenFOAM is beneficial but not required.

You should hold, or expect to obtain, a 1st or 2:1 in engineering, physics, applied mathematics, computer Science, or a related field.

Eligibility and funding

This studentship is open to UK/home and international candidates. 

After a suitable candidate is found, funding is sought from the University of Nottingham as part of a competitive process. This will cover home tuition fees and UKRI stipend.

PhD start date: October 2026

How to apply

Application deadline: 9 March 2026

To apply, please email your CV and supporting statement to Chris Ellis at chris.ellis1@nottingham.ac.uk

The University of Nottingham actively supports equality, diversity and inclusion and encourages applications from all sections of society. We - the Faculty of Engineering - provide a thriving working environment for all our postgraduate researchers (PGRs) creating a strong sense of community across research disciplines. We understand that research culture is important to our PGRs so we work closely with our Postgraduate Engineering Society and PGR research group representatives to support and enhance the postgraduate research environment.

As a PGR at the University of Nottingham you will benefit from training through our Researcher Academy’s training programme. Based within the Faculty of Engineering you will have additional access to courses developed specifically for our engineering and architecture PGRs including sessions on how to write a paper, communicating your research, and research integrity. 

We offer dedicated postgraduate study spaces, have outstanding research facilities and work in partnership with leading industrial partners.