Research Fellow in Applied AI for the Energy Transition

University of Glasgow; Glasgow; Vor Ort; GBP 50.000 - 58.000; Vollzeit

Benefits dieser Stelle

Athena SWAN commitment and supportive,

Flexible working environment

Zusammenfassung

The University of Glasgow invites applications for a Research Fellow in Applied AI for the Energy Transition within the James Watt School of Engineering. The post is full-time (35 hours per week) with UKRI-funded funding until 2029 and focuses on physics-informed machine learning for subsurface characterization to support offshore renewable energy projects.

The successful candidate will work in an interdisciplinary team of engineers, data scientists and geophysicists, exploring uncertainty

Qualifikationen

• Experience with physics-informed machine learning and uncertainty quantification.
• Strong background in AI/ML methods for geosciences or subsurface engineering.
• Ability to work across interdisciplinary teams on real-world offshore energy challenges.

Aufgaben

• Undertake research in physics-informed machine learning, uncertainty quantification, and predictive modelling for subsurface characterization.
• Develop geospatial data integration approaches to support offshore renewable energy decisions.
• Collaborate with engineers, data scientists and geophysicists within the UKRI-funded Fellowship.

Kenntnisse

Physics-informed ML

Uncertainty quantification

Predictive modelling

Geospatial data integration

Jobbeschreibung

This post is full-time (35 hours per week), and has funding available until 30th September 2029 in the first instance.

Applications are invited for the position of Research Fellow in Applied AI for the Energy Transition within the James Watt School of Engineering.

This position offers a unique opportunity to contribute to a major new UKRI-funded Fellowship at the intersection of artificial intelligence, scientific machine learning and subsurface engineering. The research focuses on developing physics-informed machine learning approaches to improve understanding of the subsurface, a critical challenge in the deployment of offshore wind and other net-zero energy infrastructure.

Prior domain experience in geotechnics, geophysics or offshore engineering is not required. We welcome applications from candidates in machine learning, artificial intelligence, computer science, statistics, data science, engineering, applied mathematics, physics or a closely related discipline.

The successful candidate will undertake research in physics-informed machine learning, uncertainty quantification, predictive modelling and geospatial data integration to improve subsurface characterisation and support decision-making for offshore renewable energy developments. The research will create opportunities for methodological advances in machine learning and uncertainty-aware modelling while addressing important challenges in offshore renewable energy and the net-zero transition.

The postholder will join an interdisciplinary team of engineers, data scientists and geophysicists led by Dr Róisín Buckley as part of a UKRI-funded Fellowship. The role offers opportunities to collaborate with leading academic and industrial partners, including researchers across the UK's AI ecosystem in data science and artificial intelligence. In addition, the postholder will have access to world-class experimental and computational facilities and will play a key role in shaping an ambitious research programme at the interface of geophysics, engineering and AI.

For an informal discussion about the post and research programme, please contact Dr Roisin Buckley on [e-mail]

All applicants must have the right to live and work in the UK or be eligible to obtain permission to work in the UK.

This role may be eligible for sponsorship under the Skilled Worker visa route, subject to meeting the relevant Home Office requirements, including any applicable tradeable points criteria. Further information can be found on the University of Glasgow’s UKVI Website.

For research and academic roles, we will also consider whether the Global Talent visa route may be available, subject to individual eligibility.

Closing date: 29th November 2026 at 23:45pm

We strongly endorse the principles of Athena SWAN, including a supportive and flexible working environment, with commitment from all levels of the organisation in promoting gender equality.

The University of Glasgow, charity number SC004401.

£50,253 to £58,225 per annum (Grade 8)