76 computer-science-"https:"-"ESPCI-Paris---PSL" "https:" "https:" "UCL" "Brunel University London" Postdoctoral positions at Aarhus University
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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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(2014). https://doi.org/10.1126/science.1253920 [2] An RNA origami robot that traps and releases a fluorescent aptamer. Science Advances (2024). https://doi.org/10.1126/sciadv.adk1250 Your qualifications
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(2014). https://doi.org/10.1126/science.1253920 [2] An RNA origami robot that traps and releases a fluorescent aptamer. Science Advances (2024). https://doi.org/10.1126/sciadv.adk1250 Your qualifications
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collaboration. Please refer to Department of Animal and Veterinary Sciences (au.dk) for further information about the department; https://anivet.au.dk/en Contact Further information on the position may be
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Development Programme targeted at career development for postdocs at AU. You can read more about it here . At the Faculty of Natural Science at Aarhus University, we strive to support our scientific staff in
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Zheng. Your competences You have academic qualifications at PhD level, for example within computational biology, bioinformatics, spatial omics, or related areas. Experience with computational imaging
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and technical-administrative staff and you have a flair for establishing collaborative relationships. Read more about the Department of Food Science at: https://food.au.dk/ The place of work is
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next‑generation insect camera traps with on‑device (edge) computing for real‑time detection and classification Collaborating in an interdisciplinary team spanning ecology, computer science, engineering
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We are seeking applicants for postdoc positions in ‘Mammalian Nuclear RNA Production and Turnover Systems’ to join us at the Department of Molecular Biology and Genetics in the research team
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at the Department of Electrical and Computer Engineering, Aarhus University, where we are advancing communication-efficient and distributed foundation model inference across the computing continuum