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lake and river water levels. In parallel, socio-environmental signals on water-related extremes and pressures will be extracted using natural language processing and text-mining techniques, drawing
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test and compare the predictions of this new configuration against existing theories (which model these structures in parallel) using synthetic case studies. These numerical tests will aim to clearly
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methodological focus will be the precise determination of the number of confined water molecules, addressed through two parallel strategies: connecting the channel to a fixed-pressure reservoir, and the
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infer excitation conditions and quantify the impact of fast, tenuous shocks on ionized line emission. In parallel, high-resolution 3D MHD simulations of fast shocks interacting with dense interstellar
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functions such as memory and object recognition, as well as consciousness and mental states. This research has several distinctive features: it employs a parallel approach involving human and non-human
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computational perspective, this requires solving billions of systems of ordinary differential equations in parallel on adaptively refined meshes, while performing interpolation and tensor operations
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Martindale or Taber tests), which aim to compress several years of wear into a limited number of test cycles. In some cases, these methods even induce damage mechanisms that differ from those observed
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a semi-industrial facility. In parallel, this PhD research will focus on investigating and optimising the process for removing arsenic from effluent, including through flocculation. Consequently
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demodulation techniques, and the development of signal-processing methods adapted to new sensing applications. The postdoctoral researcher will work within a multidisciplinary team combining
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Safety, Data Science (Information and Signal Processing), Speech, and Cognition. The unit conducts its research through 11 teams organized into 4 research departments: - Automation and System Safety