218 machine-learning "https:" "https:" "https:" "U.S" Postdoctoral positions in Denmark
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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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positions available in the areas listed here: https://math.au.dk/en/about/vacancies/postdoc/ When applying, you will be asked to indicate, which of the areas listed on the page above are of interest to you
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Postdoctoral Researcher in Natural Language Processing and Digital Humanities (18 months, full-time)
Intelligence, Machine Learning, or Computational Linguistics Digital Humanities or Linguistics with a strong computational focus Classics, History, Philology, or related humanities disciplines with documented
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includes the following tasks: Develop computer-aided design software for modular construction of switchable RNA nanostructures. Develop databases for RNA modules for automated building of atomistic models
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and teaching environment to its 37,000 students (FTEs) and 8.700 employees and has an annual revenue of EUR 1.106 billion. Learn more at www.international.au.dk/ Where to apply Website https
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includes the following tasks: Develop computer-aided design software for modular construction of switchable RNA nanostructures. Develop databases for RNA modules for automated building of atomistic models
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the ability to perform complex data analyses. Has experience with implementing computer-based experiments as well as field experiments. Has professional proficiency in English, both written and spoken
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Aarhus University (http://bio.au.dk/en) and work in the Archaea Group (https://bio.au.dk/en/research/research-areas/microbial-processes-and-diversity/archaea-group), Section for Microbiology
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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
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analysis and biomedical data analysis, with demonstrated experience in organ segmentation from medical images, using both traditional and machine learning–based methods, and creation of large segmentation