Applied AI Research Fellow - Subsurface & Net-Zero Energy

UK Energy Research Centre; Glasgow; Hybrid; GBP 50,000 - 58,000; Full time

Job summary

The University of Glasgow invites applications for a Research Fellow in Applied AI for the Energy Transition in the James Watt School of Engineering, Glasgow. You will work on physics-informed machine learning to advance subsurface characterisation for offshore renewable energy and net-zero projects.

Applicants from machine learning, AI, computer science, statistics, data science, engineering, or applied mathematics are welcome.

Qualifications

• PhD in a relevant field or equivalent research experience.
• Experience with ML methods for physics-informed modelling is advantageous.
• Strong skills in data analysis and modelling is essential.

Responsibilities

• Conduct research in physics-informed machine learning and uncertainty quantification.
• Develop predictive models for subsurface characterisation for offshore renewable energy.
• Collaborate with engineers and data scientists across the UK.

Skills

Physics-informed ML

Uncertainty quantification

Geospatial data integration

Predictive modelling

Education

PhD in a relevant field

Tools

Python

ML frameworks

Job description

The University of Glasgow invites applications for a Research Fellow in Applied AI for the Energy Transition in the James Watt School of Engineering, Glasgow. You will work on physics-informed machine learning to advance subsurface characterisation for offshore renewable energy and net-zero projects.

Applicants from machine learning, AI, computer science, statistics, data science, engineering, or applied mathematics are welcome.