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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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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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for Systems Analysis and Architecture (LAAS) within the MechaBioFluidics team, which has extensive expertise in microfluidics and microfabrication (a technology platform of the national ReNaTech network), in
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and an international collaborative network. The position will be based at the Laboratoire Ondes et Matière d'Aquitaine (LOMA), Bordeaux, France. Where to apply Website https://emploi.cnrs.fr/Offres/CDD
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). The available thesis projects are listed here: https://adum.fr/as/ed/proposition_Setab.pl APPLICATION PROCESS Applications must be submitted via the BEST application portal Deadline for applications : 23 November
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the development of advanced digital infrastructures, such as next-generation networks (6G and beyond), as well as the vulnerability of businesses and consumers to these risks. Context and Scientific Objectives
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conditions. - Determine the Drug-to-Antibody Ratio (DAR) and characterize the Drug Load Distribution (DLD), when required. - Apply, when appropriate, bottom-up, middle-up/middle-down and top-down strategies
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models, whilst respecting the specific spatial structure induced by river networks. - Cleaning, structuring and analysing historical long-term monitoring data (approx. 200 sites). - Adaptation and
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, including researchers, faculty members, engineers and technicians, as well as non-permanent staff, including fixed-term contract researchers, postdoctoral researchers, PhD students and interns, distributed
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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