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of the research project “Towards Matching Bounds in Large Deviations” (TOMABOLD), funded by the Research Council of Norway. The PhD position will focus on the large deviation analysis of probabilistic
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policy and administration, comparative politics or international relations. The successful applicant will work with the political science component of the interdisciplinary research project “Renewable
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will work with the political science component of the interdisciplinary research project “Renewable energy-driven recycling of CO2 and H2O to consumer products and fuels, (CH-CYCLE)”. The appointment is
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a gender balanced and inclusive environment. The candidate will take part in the larger Cosmoglobe project aiming to do joint end-to-end analysis of a wide range of cosmological datasets, including
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knowledge for precision medicine in brain disorders, building on advanced statistical methods and AI-tools for analysis of large-scale human genetic and neuroimaging data, to better understand how biological
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and methodological approaches as well as a feasible progress plan. More about the position The position is available for a period of 4 years. There is a 25% component of the position that is devoted
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for analysis of large-scale human genetic and neuroimaging data, to better understand how biological, psychological, and environmental factors contribute to severe mental and neuropsychiatric disorders
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Deviations” (TOMABOLD), funded by the Research Council of Norway. The PhD position will focus on the large deviation analysis of probabilistic models, and associated problems in PDE, with emphasis
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period of 4 years. There is a 25% component of the position that is devoted to teaching and administrative duties / other career-promoting work. These duties also include obtaining basic teaching
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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