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PhD Research Fellow in ML-assisted reservoir characterization/modelling for CO2 storage (ref 290702)
background in one or more of the fields of rock physics, petrophysics, seismic attribute analysis, seismic inversion (both pre- and post-stack inversions), and machine learning with geoscience knowledge
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. The main goal is to develop tools and knowledge for precision medicine in brain disorders, building on advanced statistical methods and AI-tools for analysis of large-scale human genetic and neuroimaging
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departments in Oslo, research groups at the University of Oslo, and international collaborators. The main goal is to develop tools and knowledge for precision medicine in brain disorders, building on advanced
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physics and characterization using photoluminescence (PL) measurements and ODMR. Knowledge on optical and electrical semiconductor defect characterization. Experience with data analysis using, e.g., Origin
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, geochronological, geochemical, paleomagnetic and sedimentological knowledge to reconstruct the environmental conditions and resolve fundamental questions that remain about the initial rise of atmospheric oxygen and
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probability theory or PDE theory to the level of a Master’s degree, and basic familiarity with both. Prior knowledge of specific subdisciplines for the examples listed in the description may be advantageous but
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on point defects in semiconductors for quantum technology applications. Experience with color center physics and characterization using photoluminescence (PL) measurements and ODMR. Knowledge on optical and
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education in mathematics. The appointed fellow should have expertise in probability theory or PDE theory to the level of a Master’s degree, and basic familiarity with both. Prior knowledge of specific
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, petrophysics, seismic attribute analysis, seismic inversion (both pre- and post-stack inversions), and machine learning with geoscience knowledge, preferably in seal/reservoir/overburden heterogeneities, static
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of Norway with the objective to generate novel knowledge about how to reduce inequalities in education. The interdisciplinary center integrates researchers with substantive expertise from education