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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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the start date. This position is part of the research project “Numerical Analysis of Stochastic TRANsport” (NASTRAN), funded by the Research Council of Norway. The project’s goal is to develop mathematical
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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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. 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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set to begin on February 1, 2026, although there is some flexibility in the start date. This position is part of the research project “Numerical Analysis of Stochastic TRANsport” (NASTRAN), funded by
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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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that captures heterogeneous information across the entire product lifecycle to support sustainable decision-making. Functioning as a data backbone for the circular economy, DPPs integrate numerical, categorical