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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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(PDE). Examples of models in the scope of the project include particle models, stochastic PDE and models from fluid dynamics and machine learning. Place of work is the Department of Mathematics, Blindern
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to a deeper understanding of the composition of the crust of the earth? Explore how to benefit from recent research in foundational neural models that learn from large unlabeled image datasets, also
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economic assessments machine learning or proxy-model based methods field scale simulation geological features geomechanics reactive flow The PhD fellow are not expected to master all these topics. Project
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, friendly and inspiring, and the position represents a unique opportunity for career development for a hard-working candidate. Main responsibilities Develop and apply machine learning and statistical modeling
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. In addition, you must have: a solid foundation in energy technology and a strong understanding of artificial intelligence (AI), machine learning (ML), and data-driven modeling documented experience
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-based cumulative-opportunity accessibility modelling and isochrone mapping Comparative analysis of mobility cultures (e.g., walking, cycling, public transport, car use) Accessibility and mobility
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University of Stavanger invites applicants for a PhD Fellowship in in molecular modelling and machine learning for improved subsurface utilization, at the Faculty of Science and Technology, Department
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-working candidate. Main responsibilities Develop and apply machine learning and statistical modeling techniques, including novel AI architectures, for the analysis of complex traits and precision prediction
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/english/research/groups/dsb/index.html) as part of Visual Intelligence (http://visual-intelligence.no) , Norway's leading research centre in deep learning for image analysis. Starting date as soon as