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eco-friendly sensor technologies, favoring collaborations between local industrials and academia and building innovations between local actors to create startups on disruptive technologies. Within
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trophic (phytoplankton growth and loss) variables of the Thau lagoon and the Mediterranean Sea (Station 00SETE) in an innovative way using in situ data from high-frequency automated sensors; 2) linking
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evaluation of algorithms for: perception in robotics; sensor based control and navigation ; interactive mobile manipulation; multi-sensor data modelling and fusion. This job offer takes place within
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: This project is an interdisciplinary effort at the frontier between Biology (Genetics, Genomics), Bioinformatics, Artificial Intelligence (Neural Networks) and Statistics (LMMs). The aim is to join the
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physics is especially interesting due to the presence of exotic excitations, potentially non-Abelian. The TensQHE project aims to develop modern numerical tools based on tensor networks, within an open
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, including variational auto-encoders, generative adversarial networks (GANs) and more recently probabilistic diffusion denoising models. On the other hand the CRIStAL laboratory has a strong expertise in
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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