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work will be organized around the following areas: 1. Bee detection and tracking: Development of computer vision algorithms to identify and track each bee from high-resolution images, while
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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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a breakthrough concept to upgrade existing fiber optic networks to acoustic sensor arrays, becoming a key component for managing smart cities. Except for a few applications, DAS data are typically
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algorithms for dynamic structured data, with a particular focus on time sequences of graphs, graph signals, and time sequences on groups and manifolds. Special emphasis will be placed on non-parametric
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on supervised learning using ground truth aligned with frame-based sensors, which inherently limits their temporal precision. Meanwhile, self-supervised methods—such as those based on contrast maximization—remain
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complicates both learning and inference processes. Another challenge is that dynamic structured data are generated by a variety of sensors and infrastructures that continuously produce, disseminate, and store
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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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, Intelligent Operando Measurement for Advanced BMS from the PEPR Batteries program. It will be carried out in collaboration with Jean-Marie Tarascon (Collège de France), who is developing optical sensors
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compressed sensing et la capacité à optimiser des algorithmes pour des applications temps réel, qui seront également valoriséesThe candidate should have: - A strong background in applied mathematics, including
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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