567 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "UCL" positions in Norway
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assessment at all levels (BA, MA and PhD), and to carry out administrative duties in accordance with the needs of the department. For more information and how to apply: https://www.jobbnorge.no/en/available
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levels (BA, MA and PhD), and to carry out administrative duties in accordance with the needs of the department. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/290233
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out administrative duties in accordance with the needs of the department. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/290244/tenure-track-asst-professor-assoc
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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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an international and interactive environment. Candidates must have extensive experience with: Programming (including MATLAB) and computational modelling Machine Learning (ML) methodology applied for complex data
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-year master’s degree, with a learning outcome corresponding to the descriptions in the Norwegian Qualification Framework, second cycle. The applicant must have a documented strong academic background
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hypotheses related to metabolic rate increases, energy allocation shifts, temperature-dependent bioaccumulation, and varying toxicity across biological levels. For more information and how to apply: https
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groundwater/geochemical modelling software (e.g., MODFLOW, PHREEQC). Experience with laboratory analytical methods (e.g., chromatography, mass spectrometry). Familiarity with AI or machine learning applications
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Science About the project This PhD project integrates pharmacoepidemiology, causal inference, and machine learning to study real-world treatment patterns, effectiveness, and safety of monoclonal antibodies
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the areas of stochastic analysis and computational methods towards machine learning with focus on risk-sensitive decision making and control. Techniques may include forward, backward stochastic differential