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sécuriser durablement. Cette thèse vise à contribuer à la modélisation et à la simulation assistées par intelligence artificielle des processus, organisations et flux de données intervenant dans les activités
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simulations, so that they maintain their validity. The process consists in evaluating a sequence of overlapping mental simulations to construct the future course of actions that the agent will take, based
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exemples allant des moteurs moléculaires intracellulaires à des organismes entiers. Comprendre les phénomènes collectifs qui émergent quand un grand nombre de ces agents interagissent est au cœur de la
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, automation, or autonomous systems. - Dynamic modeling and physical simulation. - Trajectory planning, navigation, or robot control. - Interest in multi-agent systems and human-robot interaction. Technical
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porous solids for the capture and/or degradation of toxic agents (or simulants) and sensors. Main activities Identification of MOFS composition Using existing databases that have already identified
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polarisation extraction from simulations. The researcher will use standalone simulation and develop the studies to provide systematics and detailed studies of many potential limiations. Simulation of background
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but with a computer agent that behaves as a partner. The contrast between the control and test conditions informs us on the strategies that are developed in a social context, compared to those simply
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 10 days ago
into the eMob-Twin software platform (emob-twin.fr), enabling the simulation and evaluation of scenarios related to charging infrastructure planning, grid integration, and vehicle-to-grid (V2G) services
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using gem5 simulator as the main execution backend for the RL agent, while also investigating the potential of RTL simulation and physical RISC-V board as complementary execution nvironments. A key aspect
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, abandonment, comments, peer interaction) Formalization of algorithms for orchestrating educational AI agents : Train RL and LLM agents and study multi-objective optimization (mastery, well-being stability) Work