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optimization-based network partitioning point to scalable, communication-aware control designs; stochastic MPC and co-design studies demonstrate methods for handling uncertainty and jointly optimizing assets and
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methods. - Strong knowledge of collaborative development tools and excellent programming skills. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR8262-ELIBLA-002/Default.aspx Work
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the environmental impact of cloud infrastructures, making this PhD topic highly relevant to national and global sustainability goals. Scientific Objectives This thesis aims to develop novel methods for deploying AI
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reconstruction - Estimation theory - computational methods and deep learning approaches. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR7249-HERRIG-026/Default.aspx Work Location(s) Number
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conservators and imaging scientists to apply the developed methods to new artworks. Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR8029-NACCHI-012/Default.aspx Requirements Research
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Discovery”, with a strong scientific and environmental ambition: developing lower-footprint AI methods for real inverse problems in nondestructive evaluation. The topic has already passed the first ENACT
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the partner teams and a theory/experiment dialog will allow to enrich the project. The person recruited will have the opportunity to use a large panel of theoretical chemistry and molecular modeling methods
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manual gestures). The SyncoGest project (2025–2030) is an interdisciplinary project conducted jointly by computer scientists (Loria – University of Lorraine / Inria / CNRS), linguists (Praxiling – Paul
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strategies. It specifically aims to develop, explore, and evaluate new statistical analysis and diagnostic methods using different data sources to improve estimates of population size, temporal trends in
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cover will be constrained using Raman spectroscopy of carbonaceous material (RSCM method), complemented by bottom-hole temperatures (BHT) and heat flow data to define present-day thermal gradients