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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 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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minimizing error and maximizing efficiency, is computationally challenging—no known polynomial-time algorithm exists to solve it optimally in all cases. Because of this complexity, researchers typically rely
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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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, sensor failures, or the aggregation of datasets from multiple sources. There is a rich literature on how to impute missing values, for example, considering the EM algorithm [Dempster et al., 1977], low
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train robust machine learning (ML) algorithms without exchanging the actual data. The benefits of such a decentralized technology over personal and confidential data are multiple and already include some
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motivated the development of Federated Learning (FL) [1,2], a framework for on-device collaborative training of machine learning models. FL algorithms like FedAvg [3] allow clients to train a common global
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through innovative solutions. Coordinated by Prof. Francesco Vitti from the Engineering department, NEXTLOGIC is funded under the HORIZON-MISS-2024-CIT-01 call: Changing urban spaces and mindsets
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of the biomedical research required, but a willingness to work as part of an interdisciplinary team is essential Ability to handle multiple assignments at one time while working independently and effectively under
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. Indeed, when multiple sources exist in the vicinity of a same sensing unit, their signatures mix and estimation of individual sources is disturbed by the other co-occurring sources. The aim of the doctoral