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to strengthen the fundamental research potential of the J. A. Dieudonné Research unit at the Assistant Prof. (Maitre de conférences) level, with a profile primarily oriented towards classical analysis and its
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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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. Processing this response provides estimates of the local variations in acoustic pressure along the fiber, over distances ranging from 40km up to 140km with some systems. This technique, called Distributed
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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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. The monitoring of telecommunications and energy production and distribution networks are characteristic examples of such time-critical applications. The project aims to propose unsupervised online CPD algorithms
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heterogeneity, i.e., the fact that clients' local datasets are in general drawn from different distributions. Statistical heterogeneity for example slows down the convergence of FL algorithms [5]. In this thesis
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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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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