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: • Strong background in modelling, including spatial and temporal/dynamic modelling, demographic modelling, Bayesian hierarchical models and/or modelling with multiple data streams • Experience with data
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, demographic modelling, Bayesian hierarchical models and/or modelling with multiple data streams • Experience with data science and biodiversity informatics, in particular handling of scientific collection
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fish. Specifically, we have been working on salmon lice in commercial aquaculture. Current efforts use machine learning (object detection) to automatically quantify parasites in the sea, with ongoing
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to observe next. By combining Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more efficient, intelligent, and impactful. You will integrate field
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measurements are most informative and guiding where, when and how to observe next. By combining Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more
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of Norway (2025-2030). It comprises more than 200 faculty members from many higher education institutions in Norway, in collaboration with numerous public and private sector partners. The primary objective
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more than 200 faculty members from many higher education institutions in Norway, in collaboration with numerous public and private sector partners. The primary objective of MishMash is to create, explore, and
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forecasting. Familiarity with ensemble methods, Bayesian approaches, and uncertainty estimation. Experience with large-scale or messy real-world data (structured and/or unstructured). Interest in or experience
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are a strength. The University of Oslo has a personnel policy objective of achieving a balanced gender composition. Furthermore, we want employees with diverse professional expertise, life experience and
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in the public sector at Employer Portal . Inclusive worklife and diversity at University of Oslo Inclusion and diversity are a strength. The University of Oslo has a personnel policy objective of