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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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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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, 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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. 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
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@inserm.fr with CC to stephanie.chambaud@u paris.fr and charlotte.bouquerel@u-paris.fr, including: • A detailed CV (including a complete list of publications). • A 2-page statement on past and current research
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(morphological patterns), based on the experts’ knowledge. Then, tools like Procrustes analysis, linear dimensionality reduction (PCA) and standard clustering algorithms are employed. A first objective of our
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postdoctoral experience(s), including at least one abroad, demonstrated through multiple publications showing ability to autonomously develop research themes • Solid experience with host-pathogen interaction
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slicing. - Develop advanced AI/ML algorithms and data analytics techniques to automate and optimise exposure requests, adapted to available resources and real-time demand. - Propose and