289 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" positions at University of Oslo
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environment spanning physics, developmental biology, advanced imaging, and machine learning. Colourbox via Unsplash Colourbox What skills are important in this role? The Faculty of Mathematics and Natural
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intelligence/machine learning skills. The candidate’s research proposal must be closely connected to the call and the research of NCEI. Excellent skills in written and oral English. Personal suitability and
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information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/292175/phd-position-on-the-history-of-medical-plastics Where to apply Website https://www.jobbnorge.no/en/available-jobs/job/292175
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Research Fellow is expected to write a doctoral thesis and collaborates closely with the other project participants involved in MA4 and in WP4.1 specifically. For more information and how to apply: https
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of illness to the ways medical knowledge, technologies, and policies shape societies. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/292473/research-assistant-position-20
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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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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 Building in 2026/2027 (https://www.uio.no/english/research/strategic-research-areas/life-science/about/building/ ) Jarli og Jordan/UiO via Unsplash Jarli og Jordan/UiO What skills are important in
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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