Applied AI Research Fellow - Subsurface Energy

University of Glasgow; Glasgow; On-site; GBP 50,000 - 58,000; Full time

Benefits offered by this job

Athena SWAN commitment and supportive,

Flexible working environment

Job summary

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

Qualifications

• 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.

Responsibilities

• 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.

Skills

Physics-informed ML

Uncertainty quantification

Predictive modelling

Geospatial data integration

Job description

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