Research Associate - Enabling CO2 Storage Using Artificial Intelligence

Heriot-Watt University; City of Edinburgh; On-site; GBP 37,000 - 47,000; Full time

Job summary

An established industry player is seeking a Research Associate to develop advanced deep learning models for CO2 storage using AI. This role involves innovative research in subsurface flow modeling, contributing to open-source projects, and publishing findings in high-impact journals. The position offers a supportive environment that values diversity and collaboration, allowing you to make a real-world impact while enjoying flexible working arrangements. Join a dynamic team dedicated to pioneering research in energy and geoscience.

Benefits

33 days annual leave

Flexible working arrangements

Inclusive environment

Support for personal growth

Qualifications

• PhD or near completion in computational science, applied mathematics, or physics.
• Experience in developing deep learning models and computational fluid dynamics.

Responsibilities

• Develop deep learning models for fluid flow in porous media.
• Disseminate research results through publications and conferences.

Skills

Deep Learning

Computational Fluid Dynamics

Open-source Libraries

Software Development Practices

Communication Skills

Education

PhD in Computational Science & Engineering

PhD in Applied Mathematics

PhD in Physics

Tools

Open-source Software

Job description

Job Description

Role: Research Associate on Enabling CO2 storage using Artificial Intelligence

Grade and Salary: Grade 7, £37,174 - £46,735 per annum

FTE and working pattern: 1FTE, 35hrs per week, Monday - Friday

Contract: Fixed Term for 12 Months

Holiday Entitlement: 33 days annual leave plus 9 bank holidays (including Christmas Eve if it falls on a weekday)

Purpose of Role

The successful candidate will develop advanced deep learning models for multi-scale flow modelling of CO2 in subsurface reservoirs, focusing on pore-to-core scale upscaling and reactive flow process upscaling. They will also contribute to various AI applications in subsurface flow modelling, including stochastic porous media generation, property prediction, proxy modelling with physics-based constraints, and coupling deep learning models with numerical solvers. The techniques developed will be open-source and validated with experimental data and simulations.

Key Duties & Responsibilities

• Develop deep learning models for fluid flow in porous media.
• Disseminate research results through publications and conferences.
• Publish open-source code repositories with documentation and tutorials.
• Participate in project meetings with team members and sponsors.

Essential & Desirable Criteria

Essential

1. A PhD in computational science & engineering, applied mathematics, physics, or related fields (or near completion).
2. Experience in developing deep learning models with open-source libraries.
3. Experience with computational fluid dynamics using open-source software.
4. Strong publication record in high-impact journals.
5. Knowledge of modern software development practices (version control, CI, testing).
6. Excellent communication and report-writing skills.

How to Apply

Applications are due by midnight (UK time) on Sunday 01 June 2025. Please submit your CV and cover letter via the Heriot-Watt online portal. We support flexible working arrangements, including part-time and job share options.

Heriot-Watt University is committed to equality and diversity, promoting an inclusive environment where merit is the basis for employment decisions. We encourage applications from all backgrounds, especially underrepresented groups. For more info, visit our equality and diversity page and Disability Inclusive Science Careers.

About the Team

The School of Energy, Geoscience, Infrastructure and Society (EGIS) at Heriot-Watt University, Edinburgh, Scotland, offers a 12-month PDRA position on the ECO-AI project, based at the Institute of GeoEnergy Engineering. Details about the project are available at project webpage.

About Heriot-Watt University

Heriot-Watt values diversity, innovation, and collaboration, providing a platform for impactful research and personal growth. Join us to contribute to real-world solutions while enjoying a supportive, inclusive community that promotes work-life balance.