109 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "Imperial College London" positions at Aarhus University
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close collaboration with a specific group (DARSA) specialized in developing and applying remote-sensing tools and innovative open-source machine-learning methods. Key responsibilities Develop effective
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to the research and infrastructure in the Plant Molecular Biology section (https://mbg.au.dk/en/research/research-areas/plant-molecular-biology ). Internal communication and teaching at the Department is primarily
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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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The Section for Electrical Energy Technology at the Department of Electrical and Computer Engineering (ECE), Aarhus University, is in a phase of rapid growth in both education and research
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Department of Management, please visit: http://mgmt.au.dk/ . Further information For further information about the position and the department, please contact Assistant Professor Gabriele Torma, email
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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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organoid culturing of animal tissue. Significant skills in the scientific communication of research results. For non-Scandinavian candidates, an effort to learn to read, write and speak Danish is a
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the described area. Take the initiative in developing the research field and collaborate with national and international academic and industry partners. Teach and supervise students at the bachelor’s, master’s
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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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grasslands and evaluation of land-use intensity, Expertise in classification with machine-learning methods, statistics, spatial analysis and land-use modeling, Experience and interest in conducting fieldwork