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algorithm that reliably simulates two-phase flow. The PhD projects will be part of developing and analyzing relevant numerical methods and implement them in an open-source framework that will be made openly
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in the context of a UiT grant that focuses on modeling spatio-temporal medical image analysis with a particular focus on learning from limited labelled data. The successful candidate will be a part of
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in the context of a UiT grant that focuses on modeling spatio-temporal medical image analysis with a particular focus on learning from limited labelled data. The successful candidate will be a part of
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“greenhouse” (warmer than present) conditions. In i2B we will retrieve new, key Arctic geological archives of past warmth and employ climate models to bring our current knowledge about a warm Arctic beyond the
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focus on exploring the immunoregulatory potential of radiopharmaceuticals in preclinical cancer models. The PhD candidate will be responsible for conducting preclinical studies, using cell culture assays
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. The goal is to contribute broadly to research on applications of AI in medicine, and in particular to the development and validation of novel computational language models, algorithms, and tools
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, self-management, cultural resources, and digital self-care and prevention tools for Indigenous elders. The project employs both quantitative and qualitative methods and utilizes models for user-centered
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://www.uib.no/en/sefas About the project/work tasks: The postdoctoral research fellow will perform quantitative data analysis using advanced techniques such as signal processing and dynamic systems modeling, and
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(NorESM). In the project, the CLM model will be using the FATES (Functionally-Assembled Terrestrial Ecosystem Simulator) terrestrial ecosystem model and the MIMICS+ module for soil carbon decomposition
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techniques such as signal processing and dynamic systems modeling, and will contribute to developing knowledge-driven decision-making models. The postdoctoral research fellow will actively participate during