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and adapted tools for the processing of signals or images acquired with biomedical sensor networks (cardiology, neurosciences) or in geosciences (seismology and marine ecology), but also in wireless
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and the optimization of the sensor. The candidate will benefit from the complementary expertise and know-how acquired between the two supervisors in nanodevices and nano-neuroelectronics (C. Delacour
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of synoptic processes can be isolated from those driven by surface conditions. Thanks to advances in ground-based remote sensing technology and algorithm development, those profile observations can now be
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analysis for more geometries and with a reduced number of sensors - Implementation of the MSE method on a cylindrical structure immersed in water and sensitivity analysis - Algorithmic and experimental
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-criteria, defining their formalization as fuzzy subsets, and characterizing their uncertainty; Integrating Machine Learning algorithms to better account for low-level sensor data (precipitation, wind-driven
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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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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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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