PhD studentship: AI-enhanced modelling of liquid hydrogen flows net-zero transportation

Area

Engineering

UK Other

Friday 01 May 2026

ENG
322

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

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.

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.

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.

• 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.
• Applicants should hold (or expect to obtain) a 1st or 2:1 in Engineering, Physics, Applied Mathematics, Computer Science, or a related field.

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

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

The Faculty of Engineering provides a thriving working environment for all PGRs creating a strong sense of community across research disciplines. Community and research culture is important to our PGRs and the FoE support this by working closely with our Postgraduate Research Society (PGES) and our PGR Research Group Reps to enhance the research environment for PGRs. PGRs benefit from training through the Researcher Academy’s Training Programme, those based within the Faculty of Engineering have access to bespoke courses developed for Engineering PGRs, including sessions on paper writing, networking and career development after the PhD.

The Faculty has outstanding facilities and works in partnership with leading industrial partners.

Please contact Chris Ellis with your CV and supporting statement to apply for this project – c