Research Associate - Enabling CO2 Storage Using Artificial Intelligence

Heriot-Watt University; Mid Calder; On-site; GBP 30,000 - 45,000; Full time

Job summary

A leading research institution in Midlothian is seeking a Research Associate to develop cutting-edge AI models for CO2 storage efficiency. The ideal candidate will have a PhD in computational science or related fields and a strong background in deep learning and fluid dynamics. Responsibilities include publishing research, participating in project meetings, and contributing to open-source initiatives. This role offers an opportunity to impact AI applications in subsurface flow modeling.

Qualifications

• PhD in computational science & engineering, applied mathematics, physics, or related field (or near completion).
• Experience with computational fluid dynamics using open-source software.
• Strong publication record in high-impact journals.
• Experience with modern software development techniques.
• Excellent communication skills and ability to produce professional reports.

Responsibilities

• Develop deep learning models for fluid flow in porous media.
• Disseminate research results in peer-reviewed journals and conferences.
• Publish open-source code repositories with techniques, notebooks, blogs, and presentations.
• Participate in project meetings with team members and sponsors.

Skills

Computational fluid dynamics

Deep learning

Software development techniques

Excellent communication

Education

PhD in computational science, engineering, applied mathematics, physics

Tools

Open-source software

Job description

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Research Associate - Enabling CO2 Storage Using Artificial Intelligence, Midlothian

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Job Reference:

85f2e2f44da7

Job Views:

12

Posted:

12.08.2025

Expiry Date:

26.09.2025

Job Description:

Purpose of Role

The successful candidate is expected to develop cutting edge deep learning models for multi-scale flow modelling of CO2 in subsurface reservoirs. Two aspects are of special interests (a) pore-to-core scale upscaling (b) upscaling of reactive flow processes at pore-scale. In addition, the successful candidates will contribute to a wide range of AI applications in subsurface flow modelling including (a) stochastic generation of porous media realizations using deep generative models (b) deep learning based property prediction using various architectures (c) Deep learning based proxy modelling with physics based losses and built-in model constrains (d) Efficient coupling of deep learning models to numerical solvers for hybrid CO2 flow modelling. The developed machine learning techniques will be open-sourced and validated across applications, experimental data, and direct numerical simulations generated by the project team.

Key Duties & Responsibilities

The successful candidate will:

• Develop deep learning models for fluid flow in porous media.
• Disseminate research results in peer-reviewed journals and conferences.
• Publish open-source code repositories with techniques, notebooks, blogs, and presentations.
• Participate in project meetings with team members and sponsors.

Essential & Desirable Criteria

• A PhD in computational science & engineering, applied mathematics, physics, or related field (or near completion).
• Experience with computational fluid dynamics using open-source software.
• Strong publication record in high-impact journals.
• Experience with modern software development techniques.
• Excellent communication skills and ability to produce professional reports.