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over the course of the project. References: - Deneu B et al (2021) Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment. PLoS Comput
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of systems in constrained environments and sensors and instrumentation. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR5214-JEAGAY-082/Candidater.aspx Requirements Research
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for the High-Luminosity LHC. Our primary responsibility is the integration of double-sided silicon sensors onto mechanical support structures (ladders), including the associated electrical, optical, and cooling
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movements can be preceded by slow movements lasting from several days to several years. These movements can be detected and tracked by satellites, either using radar or optical sensors. Since 2016, data from
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- Design pilot and data collection of MEEG and behavioral experiments with Psychtoolbox, JsPsych, Pavlovia - Univarate and multivariete analysis (RSA, encoding and decoding models) of MEEG data at sensor and
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, robustness under varying turbulence, and autonomy for distributed systems. To address this, the group integrates Artificial Intelligence into AO control loops, using deep learning to handle sensor
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underground infrastructure (sensors, passive seismic methods, etc.) and surface impacts (e.g., satellite interferometry), (v) Predictive modeling of coupled processes (e.g., reactive transport, water-rock
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they interact in a bipartite network and with abiotic environmental changes - mathematical developments - coding - tests with simulations - data analyses - paper writing - presenting results
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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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) * electromagnetic design of device using soft-ware such as comsol *clean room fabrication using both optical and e beam lithography *Optoelectronic characterization of infrared sensor (I-V, photocurrent spectrum