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research that addresses emerging security threats in AI products, particularly as autonomous agents become more sophisticated and widely deployed. Working alongside a dedicated team of security researchers
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components to execute complex reasoning and decision-making tasks. These agents are increasingly deployed in domains such as healthcare, finance, cybersecurity, and autonomous vehicles, where they interact
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intelligence systems that combine vision-language-action (VLA) modeling, robotic perception and interaction, and autonomous task execution. The Senior Research Engineer will play a key role in bridging
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systems that combine vision-language-action (VLA) modeling, robotic perception and interaction, and autonomous task execution. The Research Engineer will assist in system design, software implementation
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latency constraints. - Federated and distributed learning for RAN hardware infrastructure management. o Knowledge of autonomous AI systems based on agents. Indicative skills/experience: - Multi-Agent
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in artificial intelligence (AI) for settings involving multiple interacting decision-makers---whether autonomous AI agents, humans, or a combination of both. Applications include mixed-autonomy
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-Service (MaaS) ecosystem. The work will integrate deep reinforcement learning, autonomous agent modelling, and multi-objective optimization to enable predictive simulation, real-time resource management
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that can be connected to a micropump to perfuse them with liquid and injecting with contrast agents. The diameter of the blood vessels created with this printing technique is limited to tens of micron in
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-Efficient Fine-Tuning (PEFT), and alignment strategies (e.g., Reinforcement Learning from Human Feedback [RLHF]) for healthcare and life sciences. AI Agents and Autonomous Reasoning, including the development
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) department at Telecom Paris. Reinforcement learning (RL) has emerged as a useful paradigm for training agents to perform complex tasks. Model-based RL (MBRL), in particular, promises greater sample efficiency