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the Machine Learning and Artificial Intelligence. Solid mathematical and analytical skills. Knowledge about statistical machine learning, robotic perception, multimodal AI algorithms. Experience in programming
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, including but not limited to AI; analyzing user behaviors, perceptions, and learning outcomes with computational and/or mixed methods; and publishing in major conferences and journals in information and
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tasks: Conduct cutting-edge research in learning-based and/or model-based control and/or perception strategies for dexterous robotic manipulation in simulated and real robots Support laboratory activities
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and www.spacer.lu The candidate should develop the following tasks: Conduct cutting-edge research in learning-based and/or model-based control and/or perception strategies for dexterous robotic
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, exoskeletons and force augmentation, manipulation and dexterous manipulators, telepresence and teleoperation, industrial automation and robotics, active perception and learning, inspection robotics, hyper
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, exoskeletons and force augmentation, manipulation and dexterous manipulators, telepresence and teleoperation, industrial automation and robotics, active perception and learning, inspection robotics, hyper
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driving simulators (e.g., CARLA, SUMO) or robotic simulation environments (e.g., Isaac Sim, Gazebo). Experience with ROS or ROS 2. Experience with PyTorch or other machine learning frameworks. Hands
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involve the integration of: Advanced motion planning and control algorithms Multi-modal perception techniques (e.g., vision, tactile, force) Machine learning models for physical behavior prediction and
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, dynamical systems, statistical machine learning, and neural time-series data. The goal is to better understand principles and mechanisms underlying distributed brain network computations through the dual
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in electrical engineering, computer engineering, computer science, or similar. Strong background in communication systems, optimization, or machine learning for networked systems. Experience and