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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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karstic networks and alluvial terraces lying on the top of the foothills. The work will be mainly based on already existing and new TCN dates in order to: (1) gain an overview of the oldest remaining
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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