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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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reorientation, bimanual manipulation, and assembly Advance techniques including behavior cloning, reinforcement learning, and VLA-based reasoning, with a focus on novel contributions and publishable outcomes
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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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, 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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include deep learning, reinforcement learning, differentiable modelling and inverse design. You will implement and evaluate these methods using experimental optical systems and work towards their
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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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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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.), 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