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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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or reinforcement learning o Sensor fusion and state estimation o Motion planning and control • Excellent programming skills in Python and/or C++. • Experience with ROS
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, based on simulation models in Aspen, COMSOL, etc. c. Control theory & applications — including nonlinear systems, MPC, reinforcement learning, stability analysis, etc.
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• Excellent coding skills in python with pytorch (distributed deep reinforcement learning, Transformers, etc.) • Literature review/summarizing skills
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the following areas: Quantum algorithms Quantum stochastic processes and quantum combs Machine learning and statistical learning theory Reinforcement learning Differential privacy Model reduction and
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.), data-driven modelling and control methods (e.g., reinforcement learning, transfer learning, etc.) Proficiency in programming tools and languages, e.g., MatLab, Python, Modelica, C, ForTran, etc. We
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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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processes, stochastic control, optimization, or reinforcement learning; • Solid mathematical training and ability to work with rigorous proofs; • Familiarity with Markov decision processes, dynamic
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• PhD in Mathematics/Statistics/CS. Skills: • Proficiency in reinforcement learning, quantitative finance, risk management, and machine learning. • Strong programming skill in Python
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theory, reinforcement learning, robotics and perception. You will investigate new algorithms and methods for robust and adaptive manipulation, including learning-based control for robotic systems and