Salary: Grade 8 (£45,585-£56,021)

Contract: Full-time (35 hours per week), Fixed Term for 12 months with possible extension pending funding availability.

Purpose of Role

The successful Research Fellow in Scientific Machine Learning is expected to develop model-based control and optimization techniques for multi-scale flow modeling of CO2 in subsurface reservoirs including the development of simplified complexity models for an accelerated risk assessment and optimization algorithms. In addition, the successful candidate will contribute to a wide range of AI applications in subsurface flow modelling including (a) Deep learning proxy modelling with physics based losses and built-in model constrains (b) 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.

The successful candidate will be part of a large multidisciplinary research project on maximising CO2 storage in deep geological formations. The candidate will benefit from interactions with the project team across Heriot-Watt university and Imperial College London including: 

• Institute of GeoEnergy Engineering (IGE) at Heriot-Watt University
• Lyell centre at Heriot-Watt University
• Department of Earth Science and Engineering (ESE) at Imperial College London

Key Duties & Responsibilities 

The successful candidate will be expected to undertake the following:

• Develop deep reinforcement learning algorithms for subsurface fluid control.
• Develop effective fluid flow emulators using deep learning techniques.
• 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

Essential Criteria

Qualifications 

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

Experience

• Prior experience in developing deep learning models using open-source libraries (e.g., PyTorch, JAX).
• Prior experience with open-source simulators (e.g., The MATLAB Reservoir Simulation Toolbox, JutulDarcy and/or OpenFOAM).
• Prior experience in developing control and optimization algorithms.
• 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.

About the School

The School of Energy, Geoscience, Infrastructure and Society (EGIS) at Heriot-Watt university (HWU), Edinburgh, Scotland, has an opening for an exceptional research fellow position to work on across the different work packages of the project ECO-AI (Enabling CO2 storage using Artificial Intelligence techniques). This post will be based at the Institute of GeoEnergy Engineering (IGE). Further details about ECO-AI are available at the project webpage https://ai4netzero.github.io/eco-ai/
 

How to Apply 

Applications can be submitted until midnight on Sunday 08th of September 2024. 

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

Potential candidates who wish to discuss the post informally can contact project leader: Prof. Ahmed H. Elsheikh (a.elsheikh@hw.ac.uk)

About Heriot-Watt University

At Heriot Watt we are passionate about our values and look to them to connect our people globally and to help us collaborate and celebrate our success through working together. Our research programmes can deliver real world impact which is achieved through the diversity of our international community and the recognition of creative talent that connects our global team. 

Our flourishing community will give you the freedom to challenge and to bring your enterprising mind and to help our partners with solutions that can be applied now and in the future. Join us and Heriot Watt will provide you with a platform to thrive and work in a way that also helps you live your life in balance with well-being and inclusiveness at the heart of our global community.  

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. Heriot-Watt University values diversity across our university community and welcomes applications from all sectors of society, particularly from underrepresented groups. For more information, please see our website https://www.hw.ac.uk/uk/services/equalitydiversity.htm and also our award-winning work in Disability Inclusive Science Careers https://disc.hw.ac.uk/. We welcome and will consider flexible working patterns e.g. part-time working and job share options.  

Use our total rewards calculator: https://www.hw.ac.uk/about/work/total-rewardscalculator.htm to see the value of benefits provided by Heriot-Watt University.