82 machine-learning "https:" "https:" "https:" "https:" "RAEGE Az" positions at Aarhus University
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see: http://ecos.au.dk/en/ . What we offer The department offers: A multi-disciplinary research environment collaboration within strong research teams with extensive experience in carbon flux research
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and applied mathematics and offers a dynamic and collegial academic atmosphere. More information about the department can be found at https://math.au.dk. Place of Work and Area of Employment The place
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scientific interests and priorities and they will establish a vibrant research group utilizing external funding. They will develop new curricula and teach classroom-based as well as field-based courses and
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the following areas, biomedicine, immunology, neurology, medicine, biology, or other, related fields. Experiences in microfluidics or single-cell analysis are a plus, but not a prerequisite to learn our methods
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targeted at career development for postdocs at AU. You can read more about it here: https://talent.au.dk/junior-researcher-development-programme/ If nothing else is noted, applications must be submitted in
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(for more information see: https://ecos.au.dk/en/researchconsultancy/research-areas/marine-diversity-and-experimental-ecology). The department is, and wishes to remain, an active, dynamic, and inspiring
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collaborative relationships. Read more about the Department of Food Science at: https://food.au.dk/ The place of work is Department of Food Science, Aarhus University, Agro Food Park 48, Skejby, 8200 Aarhus N
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analysis to translate THz signals into optical material properties such as refractive index and absorption coefficient. Development of machine learning algorithms for material classification. Exploration
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Research Focus We are offering a Postdoctoral position in graph machine learning, algorithms, and graph management with particular focus on: Modeling real-world spatio-temporal energy networks Developing
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-constrained machine-learning (ML) models in simulations of turbulent flows. You are expected to contribute to research and development in data-driven methodologies for turbulence modeling in LES (i.e., wall and