49 learning-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "Inria" Postdoctoral positions at Aarhus University
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We are seeking a postdoctoral researcher to join an interdisciplinary team developing deep learning models for antimicrobial resistance (AMR) detection directly from MALDI-TOF mass spectrometry data
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Stochastics. The positions have 1st January 2027 as earliest possible start dates. There are postdoc positions available in the areas listed here: https://math.au.dk/en/about/vacancies/postdoc/ When
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, the Lundbeck Foundation, Novo Nordisk Foundation, Independent Research Fund Denmark, etc. Sun lab: https://dandrite.au.dk/people/research-groups/sun-group DANDRITE: https://dandrite.au.dk
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-time data into WRF-Chem • You will be contributing to the development of machine learning models used on data from Poleno Jupiters, applying Python and machine learning. • The position will focus
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degree programs within animal science and veterinary medicine. We offer a lively, engaged and innovative learning and study environment, which is closely integrated in the research environment. Our
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. For non-Scandinavian candidates, a commitment to learning Danish, including reading, writing, and speaking, is expected during the employment period. Contact Further information on the position may be
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, master’s and PhD degree programs within animal science and veterinary medicine. We offer a lively, engaged and innovative learning and study environment, which is closely integrated in the research
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about the project here https://www.planttip.dk/ In this already ongoing project, we are now moving into the phase of building the model, using available knowledge, data, and theories on consumer
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conduct a significant amount of work on site at the Academy, which is located on the island of Samsø (https://energiakademiet.dk/). Teaching and supervision As postdoc in the Department of Digital Design
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) to collect, extract, and standardise functional information 2. Develop computational tools that integrate evolutionary and functional information using comparative genomics and deep learning