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| Collective bargaining agreement: §48 VwGr. B1 lit. b (postdoc) Limited until: 31.03.2032 Reference no.: 5115 Explore and teach at the University of Vienna, where over 7,500 brilliant minds have found a unique
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mathematics, computational biology or a related quantitative field Strong background in deep learning for image analysis / computer vision, ideally on microscopy time-lapse data Proven programming expertise in
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| Collective bargaining agreement: §48 VwGr. B1 lit. b (postdoc) Limited until: 31.03.2032 Reference no.: 5115 Explore and teach at the University of Vienna, where over 7,500 brilliant minds have found a unique
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postdoc with interests in RNA virology and host-pathogen interactions. For detailed information about the laboratory’s research, see our website (http://www.mouncelab.com). Experience in molecular virology
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deadline Experience with urban acoustic monitoring or transportation noise assessment Programming skills in Python Knowledge of machine learning techniques applied to acoustic or environmental data
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to the adaptation of the Environmental Noise Directive for these new technologies. Your main focus will be to develop machine learning-based drone noise models that will be able to generate an accoustic footprint
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hardware Experience with atomic layer deposition and process development Experience with thin film and materials characterization Strong background in computational materials science and machine learning
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 7 days ago
and lifelong learning and enjoy exclusive perks for numerous retail, restaurant and performing arts discounts, savings on local child care centers and special rates on select campus events. UNC-Chapel
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goals and those of faculty mentor; and publication of research findings/scholarship during postdoc appointment period. Projects in the Rocha lab address the evolutionary history of populations and species
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increments; excursion theory of Markov processes; Tsirelson's theory of stochastic noises; deep/machine learning; Stein's method and the central limit theorem; copulas; actuarial mathematics). Where to apply E