351 algorithm-development-"Newcastle-University"-"Newcastle-University" positions in Norway
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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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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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Professor research work will include the following topics and tasks: Develop algorithms and theory for inversion of data collected by RIMFAX and other CENSSS instruments. Contribute to modelling, inversion
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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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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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the methodologies for preventing unintended, harmful behaviors in open-source AI models. Your work will focus on the foundational challenges of safety, from mitigating algorithmic bias to ensuring systems remain
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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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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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to develop intelligent systems that are both data-efficient and physically consistent. The successful candidate will contribute to advancing novel methodologies that integrate domain knowledge with learning