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    Research Associate on Enabling CO2 storage using Artificial Intelligence - Edinburgh, United Kingdom - Heriot-Watt University Malaysia

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    Description

    Salary: Grade 7 (£36,023 - £44,263)

    Contract: Full-time (35 hours per week), Fixed Term for 14 months

    The Lyell Centre at Heriot-Watt university (HWU), Edinburgh, Scotland has an opening for one PDRA position to work on the project ECO-AI (Enabling CO2 storage using Artificial Intelligence techniques). This post is funded through EPSRC. Further details about ECO-AI are available at the project webpage

    Detailed Description

    The successful candidate is expected to develop cutting edge deep learning models for storage site characterization to determine optimal CO2 injection and leakage risk assessment. Two aspects are of special interests: (a) Development of a framework for fast capacity estimation using AI-solver including geological uncertainties across scales, and (b) development of optimal CO2 injection and monitoring strategies using model-based reinforced learning algorithms.

    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.

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

    • Institute of GeoEnergy Engineering (IGE) at Heriot-Watt University
    • Lyell Centre at Heriot-Watt University
    • Institute of Mechanical, Process and Energy Engineering (IMPEE) at Heriot-Watt University
    • School of Mathematical and Computer Sciences (MACS) at Heriot-Watt University
    • Department of Earth Science and Engineering (ESE) at Imperial College London
    • Department of Chemical Engineering at Imperial College London

    Key Duties & Responsibilities

    The successful candidate will be expected to undertake the following:

    • Develop clear scenarios relevant for CO2 storage capacity and leakage risk assessment where AI can support uncertainty prediction and re-risking
    • Develop deep learning models for storage capacity estimation and/or leakage risk assessment from the metre to the reservoir scale.
    • 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

    Essential Criteria

    • A PhD degree in subsurface geosciences or engineering, with a strong focus on computational science & engineering, applied mathematics, physics or in a related computational field.
    • A solid understanding of subsurface carbon storage in porous reservoirs
    • Prior experience in developing deep learning models using open-source libraries (e.g., pytorch).
    • Prior experience in computational fluid dynamics or pore-network models using open-source software packages (e.g., MRST).
    • 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.

    Desirable Criteria

    • Experience in upscaling laboratory/field data relevant for storage capacity or leakage risk assessment to the reservoir scale.
    • Experience in developing nonlinear optimization algorithms
    • Experience in deep generative modelling.
    • Work experience with HPC, multi-GPUs systems and/or TB scale datasets

    How to Apply

    Applications can be submitted until midnight on the 31st of January 2024.

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

    Contact for enquiries:

    Potential candidates who wish to discuss the post informally can contact Professor Andreas Busch ( ), Prof. Ahmed H. Elsheikh ( , project lead ECO-AI) or Professor Florian Doster ( ).

    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 and also our award-winning work in Disability Inclusive Science Careers .

    We welcome and will consider flexible working patterns e.g. part-time working and job share options.

    Use our total rewards calculator: to see the value of benefits provided by Heriot-Watt University.

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