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of the sensor infrastructure itself using reverse estimation techniques.; The fellow will collaborate with the Robotics and Autonomous Systems Center team on the development, integration, and validation
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, including electrocardiograms and other wearable sensors, for subsequent application of machine learning and deep learning methods and classification of health and wellness parameters. Data acquisition, as
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sensors, for subsequent application of machine learning and deep learning methods and classification of health and wellness parameters. Data acquisition, as well as the preparation of presentations
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validation of the solutions being created. Activities will include the selection and integration of sensors and data acquisition systems, the implementation of algorithms for data compression and analysis, and
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will participate in the different stages of the research cycle, from problem analysis and algorithm development to its implementation and experimental evaluation on a real robotic platform:; ; 1. Study
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of a detailed set of models that characterize the capabilities and limitations of each type of robot in the fleet, including kinematic models, sensor capabilities, communication systems, and command and