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for modelling various cognitive processes on a neuroscientific basis, which are tested using robots. Areas of study include perception, memory, learning, cognitive development, attention, motor control and
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approaches, including human perceptual experiments, machine learning, digital signal processing, and computational models of hearing. UConn has a vibrant neuroscience community, and there are opportunities
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buildings, including reality capture using laser scanning and photogrammetry, automatic anomalies identification with Deep Learning techniques, and anomalies mapping using Ray Casting techniques. Requirement
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, located in Morris, MN. This is not a remote position. Job duties Create software for robot navigation and operation – 85% Develop and Refine the Perception System: Maintain, train, and improve machine
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manuscripts. Assists in drafting presentations on research findings. The Attention, Perception, and EXperience (APEX lab) as part of the Center for Practical Wisdom in the Department of Psychology
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areas, how these representations evolve with learning, and how real-time manipulation of neural activity affects perception and behavior. Characterize neural codes for visual object features (identity
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, Outlook, Access, Teams, and PowerPoint, HRIS databases, electronic timekeeping, and cloud-based storage systems. Able to quickly learn and train others to use new computer technology. Able to manage all
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selection criteria Solid theoretical background in robot perception and navigation. Deep foundation in modern machine learning. Solid programming skills in C++ and Python. Experience with ROS is a plus
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Inria, the French national research institute for the digital sciences | Bron, Rhone Alpes | France | 2 months ago
attention, prediction and learning, as well as the intricate coupling between action and perception. Our research combines (1) cross-species in-vivo observations of brain electrical and neurotransmitter
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well as experience in automated fabrication and mechanical characterization. A solid background in modelling and system identification is essential, with particular emphasis on data-driven and machine-learning–based