Research Fellow in Applied AI for the Energy Transition

Overview

The University of Glasgow is offering a full-time Research Fellow position in Applied AI for the Energy Transition within the College of Science and Engineering's James Watt School of Engineering. This role is tied to a UKRI-funded Fellowship focusing on artificial intelligence, scientific machine learning, and subsurface engineering. The appointed fellow will work on developing physics-informed machine learning methods, uncertainty quantification, predictive modelling, and geospatial data integration to enhance subsurface characterisation for offshore renewable energy infrastructure.

The postholder will collaborate with an interdisciplinary team of engineers, data scientists, and geophysicists led by Dr Róisín Buckley, alongside academic and industrial partners across the UK AI ecosystem. The position includes access to computational and experimental facilities to advance research in the net-zero transition. Funding for the post is available until 30th September 2029 in the first instance.

Who can apply

Candidates from fields such as machine learning, artificial intelligence, computer science, statistics, data science, engineering, applied mathematics, physics, or closely related disciplines are welcome to apply. Prior domain experience in geotechnics, geophysics, or offshore engineering is not required. Applicants must have the right to live and work in the UK or be eligible to obtain permission to work here, as the role may be eligible for visa sponsorship under the Skilled Worker or Global Talent routes subject to Home Office requirements.

What you get

The position offers a salary ranging from £50,253 to £50,253 per annum on Grade 8. The working arrangement is on-site at 35 hours per week on a fixed-term contract.

Key dates

The closing date for applications is 2026-11-30 at 23:45 pm.