579 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "University of Waterloo" positions in Norway
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. The project will collaborate closely with, and utilize existing linked data from, Regforsk at NTNU (https://www.ntnu.no/ism/forskning/regforsk#/view/publications). Deakin University in Melbourne, Australia is
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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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practical information about working and living in Norway can be found here: https://uit.no/staffmobility Application Your application must include: Cover letter explaining your motivation and research
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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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-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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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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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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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