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, high-content imaging, data science and computational modelling are used to study the biomolecular networks governing cell fate specification and cellular behaviours, such as collective migration and
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methods for modeling exposures, we will establish real-world patterns of hormonal contraceptive use and mental disorders, and identify potential moderating factors such as medication type, age at first use
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wind propulsion devices. The work will focus on data-driven modelling, routing optimization, and control strategies for wind-assisted vessels. Relevant research topics include voyage optimization under
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results in foundational neural models, where models learn from large unlabelled image datasets, but also on additional data like clinical reports or electronic health rec-ords. The work will be done in
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modelling knowledge, incorporate reliability/uncertainty, and/or explainable models. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/293458/phd-research-fellow-in-deep
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candidate with a background in aquatic sciences, environmental chemistry, biogeochemistry, or a closely related field, with experience in field measurements, modelling, or both. Special emphasis
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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 discovery. Nova
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in particular artificial intelligence (AI)—to study genetic and epigenetic alterations in cancer. ICGI is recognised for its work in digital pathology, where AI is used to develop new models
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work on adapting or developing marine foundation models. Self-supervised learning and active learning are also possible research topics. You can also focus on challenges related to modelling physics
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for simulation and modeling of wave dynamics, and for uncertainty quantification of extreme events. The project will combine stochastic mathematical models of wave physics with advanced computational methods