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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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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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. The project will leverage large-scale longitudinal data from the Adolescent Brain Cognitive Development (ABCD) Study and conduct a new longitudinal study with adolescent girls initiating HC use. For more
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in foundational neural models that learn from large unlabeled image datasets, also incorporating context from additional data such as wireline logs or well reports. You are suited for this position
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has access to excellent HPC infrastructure. For more information about LTG, please see: http://www.mn.uio.no/ifi/english/research/groups/ltg/ The successful applicant will benefit from close
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factors. The project will use large-scale longitudinal survey data linked to national prescription registries and apply causal modelling frameworks to identify potential causal pathways linking HC use
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neurodevelopmental processes, potentially elevating vulnerability to mood disorders. The project will leverage large-scale longitudinal data from the Adolescent Brain Cognitive Development (ABCD) Study and conduct a
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of large language models (LLMs). The research profile of the group is heavily machine-learning oriented and the group has access to excellent HPC infrastructure. For more information about LTG, please see
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which also include health care services . More practical information for working and living in Norway can be found here: https://uit.no/staffmobility Application Please note that the application will only
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, and how their combination can improve safety signal detection. As a PhD fellow, you will be working with large-scale longitudinal data, managing data, writing scripts, performing statistical analyses