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PhD Research Fellow in ML-assisted reservoir characterization/modelling for CO2 storage (ref 290702)
strong machine learning and numerical modelling background to add knowledge on the impact of geological heterogeneity and subsurface environments (e.g., depth, exhumation, temperature, pressure) to de-risk
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and dynamic reservoir modelling, and flow simulation. The candidate will work in a team of geologists, geophysicists, geochemists and staff with strong machine learning and numerical modelling
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, geochemists and staff with strong machine learning and numerical modelling background to add knowledge on the impact of geological heterogeneity and subsurface environments (e.g., depth, exhumation, temperature
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. Numerical simulations and theoretical membrane models will be developed, aiming to couple viscous interfacial fluid flow, elastic deformations and wetting-like processes at cellular membranes. The theoretical
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models, aiming to reduce CO₂ emissions and improve resource efficiency through enhanced data-driven lifecycle management. A DPP can be viewed as a structured, machine-readable knowledge artifact