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of human body models (HBM) able to predict human motion and postural control (both anticipatory as preparation for upcoming perturbation, and compensatory for restoring balance after perturbation). To model
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such as vehicle cabins present a lot of clutter and multipath masking the signature of the person or people to observe; and 2) the human body is a rather complicated object to observe with radar, as we want
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Challenge: Reliable and affordable sensing for driving automation. Change: Synergy between human drivers & vehicular sensing systems. Impact: Automated vehicles that can handle safety-critical
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adapt to the patient. You will thus pave the way to next-generation small- to large-scale neuromorphic architectures inspired by the human neocortex, targeting low-footprint adaptive devices such as
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of seamless interaction in human populated environments, primarily due to the high complexity of modeling and reasoning over the effect that robot actions have on other robots and humans, and the
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Do you posess expertise in statistical methods and techniques, sociology, mathematics, or the social sciences, coupled with a keen interest in digital technologies and human rights? The Faculty
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The research programme DeepNL aims to improve the fundamental understanding of the dynamics of the deep subsurface under the influence of human interventions and the resulting effects
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of human body models (HBM) able to predict human motion and postural control (both anticipatory as preparation for upcoming perturbation, and compensatory for restoring balance after perturbation). To model
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Challenge: Human Behavior Recognition and Generation with Large Contextural Behavior Model Change: Incorporate context for human behavior recogintion and generation Impact: Advanced techniques
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supervised by Dr. Georgios Papaioannou (Intelligent Vehicles Section at Cognitive Robotics Department). The intelligent vehicles section consists of three groups (Machine Perception, Human Factors and