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)." Successful applicants for this position will have the opportunity to work on cutting-edge research at the intersection of reinforcement learning (RL), world models, embodied intelligence, and human learning
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robotic middleware (e.g., ROS, MoveIt) and hardware integration. Knowledge of machine learning, reinforcement learning, or vision-language models for robotics is a plus. Hands-on experience with robotic
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control or learned policies on physical robotic hardware. Background in modern robot learning and policy learning methods, with hands-on experience in deep learning and reinforcement learning frameworks
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