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geochemical field data and backed by numerical modelling of the lithium system. The focus is on small-scale volcano-sedimentary and closed basin sedimentary systems in Chile and the USA with existing geological
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multiphysics models to investigate aquifer-based compressed air energy storage (CAES) systems. The research will involve coupling fluid flow, heat transfer, geomechanics, and potentially reactive transport
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, stiffness loss, damage evolution, and transient creep interact under coupled loading. The project will develop temperature-dependent constitutive models informed by numerical simulation. Machine learning
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: machine/deep learning, numerical modelling, statistics, optimisation, scientific computing • Ability to work across disciplines and collaborate with academic and industrial teams Desirable: • Experience in
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of strong quantitative skills and interest in computational modelling. Basic programming/scripting ability (e.g., Python, MATLAB, Julia, C/C++) and confidence working with numerical tools. Desirable
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-driven AI models that capture the underlying process–structure–property relationships governing metal additive manufacturing. By combining mechanistic modelling, in-situ sensing, and machine learning
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model systems to explore experimentally pathways of carbon flow from gaseous sources into microbiomes. This project will use state-of-the-art stable isotope tracer experiments (13C) to track
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scientific computing, to name a few. Modern LC applications rely heavily on accurate and efficient mathematical modelling of confined LC systems. Typical questions are - can we theoretically predict physically