Research Fellow in Applied AI for the Energy Transition Job in UK 2026

Ready to apply machine learning to the offshore wind revolution? The University of Glasgow seeks a Research Fellow in Applied AI for the Energy Transition. Skilled Worker sponsorship may be available, and the Global Talent route may also be considered. If physics-informed AI and net-zero engineering excite you, this role offers meaningful impact.

About the Role

You will join a new UKRI-funded Fellowship at the James Watt School of Engineering. The research focuses on physics-informed machine learning to improve understanding of the subsurface. This work supports the deployment of offshore wind and other net-zero energy infrastructure.

Additionally, you will work on uncertainty quantification, predictive modelling, and geospatial data integration. You will join an interdisciplinary team led by Dr Róisín Buckley. The post is full-time at 35 hours a week, with funding until 30 September 2029, and pays £50,253 to £58,225.

About the Hiring Firm

The University of Glasgow was founded in 1451 and is the fourth-oldest university in the English-speaking world. It is a member of the Russell Group of research-intensive universities. Its main campus has been at Gilmorehill in Glasgow’s West End since 1870. Engineering has been taught there since 1840, when Queen Victoria founded the UK’s first Chair of Engineering.

Its alumni include James Watt, Adam Smith, and Lord Kelvin. The James Watt School of Engineering is one of its schools within the College of Science and Engineering.

Job Duties

• Research physics-informed machine learning for subsurface characterisation
• Develop uncertainty quantification and predictive modelling methods
• Integrate geospatial data to support offshore renewable decisions
• Collaborate with engineers, data scientists, and geophysicists
• Work with academic and industrial partners across the UK’s AI ecosystem
• Help shape an ambitious research programme

Job Requirements

• Background in machine learning, AI, computer science, statistics, or data science
• Engineering, applied mathematics, physics, or a closely related discipline also accepted
• Domain experience in geotechnics, geophysics, or offshore engineering is not required
• Right to live and work in the UK, or eligibility to obtain permission

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Conclusion

This role suits data-driven researchers who want their work to support clean energy. With world-class facilities and funding to 2029, the opportunity is substantial. For machine learning researchers seeking real-world impact, Glasgow offers an inspiring home.

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