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, particularly robotic arms. Familiarity with human-robot interaction and reinforcement learning is a plus. Demonstrated ability to conduct independent research and contribute to collaborative projects. We regret
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MUJOCO Reinforcement learning for trajectory planning Job Requirements: PhD in Mechanical engineering, Electrical engineering, or related fields Good publication track record Creative mind, with a problem
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of scholars and a science support team. To complement this team, we are looking to hire a Postdoctoral Fellow in Social Reinforcement Learning and Human‑AI Hybrid Systems (E13 TVöD , 100%; 39 hours/week) The
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Model Development Support development and evaluation of learning-based control policies using reinforcement learning, imitation learning, and visuomotor learning approaches. Assist in model training
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-making methods powered by supervised and reinforcement learning, which aim at trustworthiness in AI-assisted human control with augmented cognition, hybrid human-AI co-learning and autonomous AI, with
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algorithms such as PINN, SKINN. Building agentic economic world models that are incorporated with theory and knowledge structures and connecting them with reinforcement learning and generative modelling
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via human-in-the-loop feedback and reinforcement learning mechanisms in collaboration with other work packages. Maintain high software engineering standards through rigorous testing, version control
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. Advanced machine learning, reinforcement learning, and agent-based optimization techniques will be developed to reduce voltage deviations, cut active power curtailment, and improve system adaptability under
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) and hardware integration. Knowledge of machine learning, reinforcement learning, or vision-language models for robotics is a plus. Hands-on experience with robotic arms (e.g., UR5, Franka Emika
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communications, IoT, or edge computing. Demonstrated proficiency in software API and algorithm development of edge intelligence algorithms using Python. Knowledge of machine learning or reinforcement learning