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) at the University of Southern Denmark invites applications for a postdoctoral research fellowship position within the field of neuro-morphic reinforcement learning to be filled earliest by 1 October 2026 for a period
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reinforcement learning methods for cooperative source localization in dynamic marine environments. Research will address distributed information fusion, decentralized coordination, and control with event
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REQUIREMENTS The College of IST is seeking applicants for part-time job of Undergraduate Researcher in Multi-Agent Reinforcement Learning. Job duties to include: Conduct research on multi-agent deep
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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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This project focuses on simulating the organic growth of criminal communication networks by leveraging techniques such as Reinforcement Learning and Graph Theory. The goal is to curate a synthetic
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learning, algorithms, and programming. Prior exposure to reinforcement learning or human-robot interaction is highly desirable, though motivated candidates with a strong grounding in AI/ML and willingness
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Details Title Pre-Doctoral Fellow: Reinforcement Learning for Large Scale Foundation Model Post-Training School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area
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As Austria's largest research and technology organisation for applied research, we are dedicated to make substantial contributions to solving the major challenges of our time, climate change and digitalisation. To achieve our goals, we rely on our specific research, development and technology...
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, non-linear contact interactions, and varying surface conditions. Reinforcement learning (RL) offers a promising approach to develop adaptive and robust control policies, but training on physical
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parameterization and shows limited adaptability to different vehicle types and operating conditions. The aim of the master's thesis is to investigate an end-to-end reinforcement learning approach for path tracking