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university, the internationally accredited registrar and classification society DNV, and Cancer Registry of Norway. You will be analyzing and developing algorithms for privacy preserving health registry data
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are differentially private algorithms for statistical model parameter estimation under different trust relations. About the project The position is funded by the Norwegian Research Center for AI Innovation and will be
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that gives you the opportunity to develop a strong profile as researcher. You will also gain valuable experience that might be relevant for your further career. Large language models (LLMs) are today a central
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at SLATE. The position is for a fixed term of four years, with the inclusion of career development work (e.g., teaching, project work) accounting for 25% of the entire period, and is financed by University
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of career development work (e.g., teaching, project work) accounting for 25% of the entire period, and is financed by University of Bergen. The position is open to an incoming candidate, se LEAD AI mobility
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Trondheim, Norway, but the Researcher-project is of both academic and industrial relevance, and it will therefore be developed in close cooperation between NTNU in Trondheim and the company Petronas, which is
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methods for developing games and web services that can be used to support parents, teachers and students to understand and compare different AI systems and services Decision support services to help
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develop new deep learning algorithms for spatio-temporal medical image analysis with particular focus on learning from limited labelled data. General information about the position. The position is a fixed
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, Natural Language Processing, and structured knowledge representations. As a researcher within Integreat, you will contribute to developing next-generation Machine Learning for advanced data analysis
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, Natural Language Processing, and structured knowledge representations. As a researcher within Integreat, you will contribute to developing next-generation Machine Learning for advanced data analysis