144 machine-learning "https:" "https:" "https:" "https:" "U.S" Fellowship research jobs in Norway
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PhD Research Fellow in ML-assisted reservoir characterization/modelling for CO2 storage (ref 290702)
-build ups in potential multi-site storage licenses. The research will help to suggest best practices for machine learning integration in de-risking CO2 storage sites. We seek a candidate with a strong
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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 apply: https://www.jobbnorge.no/en/available-jobs/job/291009/phd-research-fellow-in-criminology Where to apply Website https://www.jobbnorge.no/en/available-jobs/job/291009/phd-research-fellow-in-cr
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implement new nonlinear iterative solvers, with the goal of exploiting models of various complexity, ranging from high-performance computing, via reduced-order models to data-driven (machine-learned
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integration of welfare technologies in the care systems for elderly people. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/292367/phd-research-fellow-in-human-robot
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collection and employ relevant machine learning methods for data analysis and sensor fusion. The PhD Research Fellow will collaborate closely with another PhD Research Fellow at the Faculty of Health and
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doctoral degree in cryptography or a related area. Good oral and written presentation skills in English. The ability to teach courses at the department (https://wiki.math.ntnu.no/emner ). PLEASE NOTE: For
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of Norway. The position is affiliated with the Center for Language, Brain & Learning (C-LaBL), more specifically the Brain domain. C-LaBL is financed by the Trond Mohn Foundation 2024-2029. The position is a
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the competencies the Postdoctoral Fellow is expected to acquire during the employment period. The Postdoctoral Fellow will also have access to career guidance throughout the postdoctoral period. Candidate A: One
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, and the military. Both quantitative and qualitative approaches would be relevant, and comparative approaches (cross-sector, cross-institutional, cross-national, or other) are welcome, but not required