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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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outputs, and other scholarly measures of impact. Strong demonstrable background in machine learning including published work. Must have demonstrable experience in building AI models for directed evolution
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: https://academicpositions.harvard.edu/postings/14695 Applications accepted through March 3rd. Share this post: Tags: Teaching Opportunity , Unique Postdoc Opportunity Return to blog Categories
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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
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. The focus of this position is developing methods to disentangle dynamic, multiscale ecological signals from large, noisy observational data. This work lies at the interface of statistics, machine learning/AI
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) | Working hours: 20,00 | Classification CBA: §48 VwGr. B1 lit. b (postdoc) Limited contract until: 31.03.2032 Job ID: 5115 Explore and teach at the University of Vienna, where over 7,500 brilliant minds
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, 10 lecturers, 20 postdocs, and 60 doctoral students. Our department has a welcoming culture which nurtures innovativeness and communication among our international and diverse faculty. The department
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The Franciszek Górski Institute of Plant Physiology Polish Academy of Sciences | Poland | about 1 month ago
qualifications Experience in one or more of the following areas will be considered an advantage: RNAseq analysis pipelines, systems biology, modelling biological processes, or machine learning approaches, redox
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). Specializations in the department range from mathematical statistics, computational statistics, and machine learning to the development of statistical methods for astrophysics, ecology, economics, epidemiology