63 machine-learning-"https:" "https:" "https:" "https:" "https:" positions at University of Oslo
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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
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of AI and in particular machine learning (ML). As today’s mainstream AI/ML workloads often resort to large-scale and energy-hungry supercomputers, it is necessary have a more critical look at how HPC
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, including individually tailored career development plans with formal supervision and project-based learning. Secondments, consortium meetings, and workshops will provide hands-on experience in collaborative
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educational system. Strong background in molecular modeling, molecular dynamics simulations, or computer-aided drug design. Proven record of programming language through publicly available Github/Gitlab
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dynamics simulations, or computer-aided drug design. Proven record of programming language through publicly available Github/Gitlab or similar repositories. Experience in the cell biology lab Fluent oral and
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recently funded centre of excellence (Integreat). Integreat collects scientists from statistics and computer science and offers a flourishing machine learning community, including many PhDs and PostDocs
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of the post. Candidates without a master’s degree have until 1st of July 2026 to complete the final exam. Strong programming and artificial intelligence/machine learning skills. The candidate’s research
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kind of machine learning algorithm, provides more accurate data than traditional data collection methods, e.g. paper-based surveys. This data is valuable to several stakeholders: i) architects and urban