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Research Fellow in Deep learning on image data for subsurface imaging Apply for this job See advertisement About the position Position as PhD Research Fellow in machine learning available at Department
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or equivalent in cognitive science, computational science or machine learning with a STEM focus. Foreign completed degrees (M.Sc.-level) must correspond to a minimum of four years in the Norwegian educational
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, with interests spanning a broad range of areas - including statistical machine learning, high-dimensional data and big data, computationally intensive inference for complex models, causal inference and
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involve close collaboration between molecular biologists, neuroscientists and machine learning experts in our research group as well as with collaborators at e.g. Harvard University and University
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of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; and statistical machine learning. More about the position The position is part of the project
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Research Fellow in Deep learning for imaging Apply for this job See advertisement About the position Two (2) positions as PhD Research Fellow in machine learning available at the Department for Informatics
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of machine learning (ML) techniques and density functional theory (DFT) simulations as well as experimental validation. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs/job
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position in the project ChemFrac (Nanoscale controls of reaction-induced fracturing from first principles) funded by the Research Council of Norway. The successful candidate will train machine learned force
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. The successful candidate will analyze large datasets from Earth system models using machine learning techniques, and develop and apply emulators to assess hysteresis in Earth system model response. The PhD fellow
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PDEs and equations subject to stochastic perturbations, integrating approaches from machine learning algorithms, transport theory, and optimization. Examples of relevant equations include, but are not