Job Description

Contract: Full-time (35 hours per week), Fixed Term funding available until the 31st of March 2025.

We have the opportunity for 2 Research Associates to join this research project. Interviews are due to take place on the W/C 17th of July 2023.

The successful candidates are 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. 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 (e) Effective optimization techniques for physics constrained implicit neural models (f) Efficient coupling of deep learning models to numerical solvers for hybrid CO2 flow modelling. The developed machine learning techniques will be open-sourced and be validated across a wide range of applications and on experimental data and direct numerical simulations generated by the project team.

Key Duties & Responsibilities

The successful candidate will be expected to undertake the following:

• Develop deep learning models for upscaling two-phase fluid flow in porous media.
• Develop uncertainty aware emulators for reactive flow models.
• Disseminate research results in peer reviewed journals and interdisciplinary conferences.
• Publish open-source code repositories demonstrating all developed techniques and associated computational notebooks, blogs and presentation materials.
• Organize and lead Hackathons as a part of ECO-AI project activities.
• Participate in regular project meetings with team members and project sponsors.

Education, Qualifications and Experience
Qualifications

• A PhD degree in computational science & engineering, applied mathematics, physics or in a related computational field.

Essential Criteria

• Prior experience in developing deep learning models using open-source libraries (e.g., pytorch).
• Prior experience in computational fluid dynamics using open-source software packages (e.g., OpenFOAM).
• Strong track record of publications in high impact scientific journals.
• Working experience in modern software development techniques (version control, continuous integration, software testing, etc).
• Excellent verbal and written communication skills, and ability to write professional reports.

When applying, please include a cover letter addressing these selection criteria.

How to Apply

Applications can be submitted until midnight on the 13th of July 2023.

Please submit via the Heriot-Watt on-line recruitment system (1) Cover letter describing their interest and suitability for the post; (2) Full CV

Potential candidates who wish to discuss the post informally can contact project leader: Prof. Ahmed H. Elsheikh ( ) or the work-package leaders Dr Kamaljit Singh ( ) and Dr Hannah Menke ( ).

Heriot-Watt University is committed to securing equality of opportunity in employment and to the creation of an environment in which individuals are selected, trained, promoted, appraised and otherwise treated on the sole basis of their relevant merits and abilities. Equality and diversity are all about maximising potential and creating a culture of inclusion for all.

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