314 machine-learning "https:" "https:" "https:" "https:" "https:" "University of St" Fellowship positions in Norway
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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
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experience in computational modelling, reaction mechanisms, and machine learning for catalyst design and discovery. Nova is also a Principal Investigator at the Hylleraas Centre for Quantum Molecular Science
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ADNI studies The position is primarily focused on advanced statistical analysis and data integration. While machine learning and computational approaches may be applied where appropriate, the core
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topics include (a) AI, machine learning, and large language models for measurement challenges (e.g., for small-sample calibration or for accelerated algorithms), (b) identifying and investigating aberrant
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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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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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of CREATE. The successful candidate will conduct advanced methodological and psychometric research. Potential topics include (a) AI, machine learning, and large language models for measurement challenges (e.g
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Science Convergence Environment that brings active matter physics, cell biology, and machine learning to address the fundamental processes guiding the earliest stages of mammalian embryo development. Early
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of Computer Science and affiliated with the Information Systems and Human–Computer Interaction (ISCHI) research group. Your immediate leader will be the unit leader of the Information Systems and Human–Computer
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resilience of bridges under climate change-induced hazards such as flooding, scour, and debris impacts. The research aims to develop advanced numerical models and machine learning tools to predict loads