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Inria, the French national research institute for the digital sciences | Villeurbanne, Rhone Alpes | France | about 1 month ago
transmit data to tools or other agents that are not fully trusted. Their autonomous and loosely defined interactions can lead to accidental disclosure, malicious extraction of private information, misuse
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robotics. Specific application areas of focus are long-term autonomous missions in large and uncertain environments, semantic mission planning with foundation models, agentic task decomposition and event
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, but also encounter AI-mediated contributions from colleagues. Messages may be written, filtered or delivered with AI, while autonomous agents increasingly participate in work processes and collaboration
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robotics. Specific application areas of focus are long-term autonomous missions in large and uncertain environments, semantic mission planning with foundation models, agentic task decomposition and event
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learning-based surrogates for physical systems LLMs and scientific agents – large language models that autonomously reason, plan and execute scientific workflows AI for engineering design – LLM-driven agents
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exploited by intelligent physical systems, such as robots, vehicles, and distributed autonomous agents, to perceive and interpret their environment and support timely physical action. A central objective will
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Industrial Software. Key Responsibilities Scientific Research: Conduct innovative research in core directions of Industrial Intelligent Agents, including but not limited to: Autonomous Decision-Making Systems
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Are you interested in exploring how multi-agent aerial manipulation can contribute to construction and working at the intersection of robotics and machine learning? Job description Advancements in
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of reinforcement learning agents understandable to humans, leading to improved transparency, trust, safety, and regulatory compliance in high-stakes decision-making systems. Potential application domains include
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actively on the preparation and defence of a PhD thesis in the field of continual reinforcement learning. Continual reinforcement learning studies how agents can learn across a sequence of changing tasks