247 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" "U.S" uni jobs in Denmark
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of the Department of Business Humanities and Law are organized into different research Units: Entrepreneurship, Ethics and Leadership; Governance, Culture and Learning; and CBS Law. Faculty within these groups have
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at The Technical Faculty of IT and Design invites applications for a fully funded PhD stipend in the area of Human-Computer Interaction, generative AI, creativity research and knowledge worker studies. The position
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At the Department of Culture and Communication, the Faculty of Social Science and Humanities, Aalborg University, a position as an assistant professor in Emerging Digital Technologies for collaborative learning will
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Are you passionate about compression, analytics, and machine learning for the Internet of Things and can you contribute to the development, operation and support in cutting-edge projects in strong
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aligned to the Science Team ‘Food Quality Perception and Society’ (FQS) at the Department of Food Science http://food.au.dk/en/foodresearch/science- teams/food-quality-perception-society/. The Science Team
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. Please refer to http://mbg.au.dk/ for further information about The Department of Molecular Biology and Genetics and to https://nat.au.dk/ and http://www.au.dk/ for information on Faculty of Natural
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support schools, day-care centres, and youth education in creating conditions for engagement, learning, and mental health. The role of psychology in policy and governance, including how psychological
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computational biology with a strong focus on development of computational and statistical methods, particularly within machine learning and artificial intelligence. The applicant must have earned a PhD degree and
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process systems engineering. The position aims to advance physically consistent and predictive thermodynamic modeling, including the integration of advanced machine learning methods, to support process and
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knowledge of process systems engineering. The position aims to advance physically consistent and predictive thermodynamic modeling, including the integration of advanced machine learning methods, to support