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suitable data models [CSC+23]. Objectives As far as the design of efficient numerical algorithms in an off-the-grid setting is concerned, the problem is challenging, since the optimization is defined in
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, the post-doctoral fellow will consider designing distributed learning algorithms for streaming manifold-valued data. Experiments will be carried out on urban, coastal, and underwater DAS data. The novelty
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. The Department of Computer Science (DCS), headed by Prof. Dr. Jean-Sébastien Coron ( jean- ), is opening an Assistant / Associate Professor (depending on qualifications) position in Theoretical Computer Science
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frameworks, as they impose minimal, if any, assumptions about the underlying data distribution, making them more effective for detecting a wide range of changes. The CPD algorithms will be designed
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fait appel à des « pipelines » d'analyse qui associent les technologies de séquençage innovantes aux algorithmes bioinformatiques pour assembler, annoter et comparer les génomes viraux. Le projet
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in the laboratory For further information, please contact Prof. Stephanie Kreis: Your profile Bachelor or Master Degree in Biological Sciences or related discipline Previous professional experience as
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projected new Master's programme and to teach within the doctoral and lifelong learning programmes of the university For further information about the role, please contact: Prof. Dr. Boris Traue, Director of
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interdisciplinary, and together we contribute to science and society. Your role The successful candidate will join the recently established Chemical and Molecular Neurobiology group, led by Associate Prof. Ivana
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of the university For further information about the role, please contact: Prof. Dr. Boris Traue Email: Your profile A doctorate in sociology, social work, education, anthropology or related fields A minimum of three
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learning community. Basically, once the observed data is identified with a probability distribution (possibly the empirical mass function), optimal transport allows to consistently assess the similarity