558 algorithms-"Multiple"-"Prof" "NTNU Norwegian University of Science and Technology" Fellowship positions
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doctor (ph.d.) in artistic development work at the Norwegian University of Science and Technology (NTNU) for general criteria for the position. Preferred selection criteria The following qualities and
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for recruitment positions and Regulations concerning the degrees of philosophiae doctor (ph.d.) in artistic development work at the Norwegian University of Science and Technology (NTNU) for general criteria
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development work at the Norwegian University of Science and Technology (NTNU) for general criteria for the position. Preferred selection criteria Motivation and willingness for interdisciplinary research and
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for Multi-Agent Decision-Making, https://oceanerc.com ). This timely project will develop statistical and algorithmic foundations for systems involving multiple incentive-driven learning and decision-making
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Centre for Advanced Robotics Technology Innovation (CARTIN) is looking for a candidate to join them as a Research Fellow. Key Responsibilities: Develop novel algorithms for multi-agent inverse
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– Norwegian University of Science and Technology, USN - University of South-Eastern Norway, and UniCA – university of Cagliari. There will also be several associated PhDs from closely related projects. As a
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researcher in natural language processing and large language models to work with a team from multiple disciplines of machine learning and artificial intelligence to develop multimodal large language models
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. The successful candidate will play a pivotal role in a project centered around variational quantum algorithm in the near-term, especially on innovating advanced error mitigation or detection techniques to solve
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The Society offers postdoctoral fellowship grants to unusually promising recipients of M.D., Ph.D. or equivalent degrees when it appears that the program of training to be supported by the grant will enhance the likelihood that the trainee will perform meaningful and independent research...
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conditions. Our work combines traditional statistical methods with advanced artificial intelligence algorithms to identify patterns in disease. We also use qualitative methods to understand lived experiences