76 machine-learning-"https:"-"https:"-"https:"-"https:"-"RAEGE-Az" Fellowship positions at University of Oslo
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modelling knowledge, incorporate reliability/uncertainty, and/or explainable models. The position is in the Digital Signal Processing and Image Analysis Group, Section for Machine Learning, Department
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-learning-for-medical-image-analysis Where to apply Website https://www.jobbnorge.no/en/available-jobs/job/293458/phd-research-fellow-in-de… Requirements Research FieldComputer scienceEducation LevelMaster
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research activity. In this project, you will develop fundamental machine learning methods and apply them in an interdisciplinary research environment spanning physics, neuroscience and computational science
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four years are expected to acquire basic pedagogical competency during their fellowship period within the duty component of 25 %. Project description and work tasks Particle accelerators are engines
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be appointed for more than one Postdoctoral Research Fellowship at the University of Oslo. Postdoctoral fellows who are appointed for a period of four years are expected to acquire basic pedagogical
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that are tailor-made to the research questions and data of CREATE. The successful candidate will conduct advanced methodological and psychometric research. Potential topics include (a) AI, machine learning, and
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and data integration. While machine learning and computational approaches may be applied where appropriate, the core emphasis of the role is on population-level data analysis, interpretation, and
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in knowledge representation, in particular, logics for multi-agent systems. Many of the researchers of the DKM group are also affiliated with the Norwegian Centre for Knowledge-driven Machine Learning
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the Section for Catalysis and Organic Chemistry at the Department of Chemistry. The group has extensive experience in computational modelling, reaction mechanisms, and machine learning for catalyst design and
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: OUH - Cell and tissue dynamics (Bøe) Project description GENESIS is a newly established Life Science Convergence Environment that brings active matter physics, cell biology, and machine learning