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of the successful candidate is to perform bioinformatic analyses of metagenomic sequencing data for viruses present in wastewater (reference-based alignment pipelines, variant detection, and de novo assembly). He
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inconsistencies between data sources, and improve knowledge of underground water networks. Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR8188-SALBEN-004/Default.aspx Requirements Research
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the context of a PEPR project, in collaboration with eight CNRS and CEA laboratories working on the future of electrical grids. More specifically, it is linked to WP5, dedicated to cybersecurity and network
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interfacial phenomena. The project is part of the ERC Starting Grant EMBIOMO, which focuses on the dynamics of embolism propagation within leaf vascular networks and on the development of biomimetic models
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addition to applicants’ personal networks, applicants may find Breton researchers’ expressions of interest to supervise a Bienvenüe+ fellow on the programme website. SELECTION PROCESS First step: eligibility check Second
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optimize hardware neural networks made of approximately one hundred magnetic tunnel junctions, with radio-frequency inputs, in order to classify RF signals directly in the physical domain. Chains of magnetic
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intelligence (AI) is used not merely to optimize control but to discover the physical principles governing nonlinear quantum dynamics. I. SCIENTIFIC VISION Conventional quantum architectures — superconduct- ing
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, which brings severe problems in the city infrastructure management. Addressing these issues requires efficient detection to minimize the impact on public safety. There are several methods for detecting
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structure and hydrogen migration pathways. Integrated Approach and Modeling Framework The project is based on a multiscale observation strategy that combines dense nodal seismic networks, distributed acoustic
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 months ago
. Leglaive, L. Girin, X. Alameda-Pineda, and R. Séguier, "A multimodal dynamical variational autoencoder for audiovisual speech representation learning," Neural Networks, 2024. 10. A. Ballou, X. Alameda-Pineda