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-27778-post-doc-reinforcement-learning-fo… Requirements Additional Information Work Location(s) Number of offers available 1 Company/Institute Ulm University Country Germany Contact Website https://www.uni-ulm.de/in/bmt/
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data. Manage preferences for further information and to change your choices. Accept all cookies Reject optional cookies Skip to main content Postdoc Position in Neuro-morphic Reinforcement Learning
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https://www.uni-ulm.de/in/bmt/institut/stellenangebote/reinforcement-learning-f… Work Location(s) Number of offers available 1 Company/Institute Universität Ulm Country Germany City Ulm Geofield Contact
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approaches, including diffusion models and reinforcement learning, for navigation decision-making, trajectory generation and adaptive robot behavior. Key Responsibilities: Design and develop navigation
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field of quantized reinforcement learning to be filled earliest by 1 November 2026 for a period of two years. About the Project: The project focuses on the theoretical grounding and algorithmic
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reinforcement learning for general manipulation based on vision-language-action models Develop efficient data sampling policy for reinforcement learning reduce the data demand for policy learning Develop a
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Doctoral Researchers in Reinforcement Learning Employer AALTO UNIVERSITY Location Finland (FI) Salary 3143 €/month Closing date 23 Oct 2026 View more categories View less categories Job Type Research
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Max Planck ETH Center for Learning Systems | New Germany, KwaZulu-Natal | South Africa | 19 days ago
, Natural Language Processing, Neuroinformatics, Optimization, Physical AI, Probabilistic Models, Reinforcement Learning, Robotics, Security and Privacy, Smart Materials, Social Questions, Soft Robotics
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, we are looking for an individual with a strong interest and the skills needed to build AI agents based on reinforcement learning with open-source LLMs or VLMs. Required skills include extensive
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Your Job Develop a reinforcement learning (RL) controller for a liquid–liquid gravity settler, trained entirely offline in a simulated environment Use existing physics-informed neural network (PINN