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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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the installation, use, and interpretation of IMU sensors. 2. Interview The first three candidates classified according to the scientific merit (SM) may be invited for an interview, for which a grade will also be
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knowledge of adaptive optics systems, including wavefront sensors, deformable mirrors, and real-time wavefront correction algorithms. Familiarity with optical systems, particularly in high-resolution contexts
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; develop and validate an astrodynamics-based orbit determination algorithm using TFC, including hybrid solutions with stochastic filters (eg, EKF or UKF); integrate and calibrate optical sensors and develop
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Polarimetry, among others; b) Experience in the application of spectroscopy techniques for testing and validating sensors; c) Experience in material classification using Artificial Intelligence algorithms. 8.2
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;Knowledge in the collection, processing, and analysis of data obtained from physical activity and exercise monitoring technologies (e.g., wearables, sensors, or equivalent systems);Knowledge of exercise