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Field
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machinery. These tasks include the verification of backlash detection and monitoring strategies formulated from the integration of multibody models and sensor data, as well as their subsequent development
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calibration of force sensors (FSRs), inertial sensors (IMUs), and distance sensors (e.g., LRF or depth camera). ii) Assisting in the design and testing of miniaturized and low-power electronic circuits
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-based prediction. This includes instrumenting a high-power APS torch with video and acoustic sensors, developing machine learning algorithms for feature extraction, and building predictive models
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funding, Profs. Himanshu Gupta and CR Ramakrishnan conduct research in the general area of quantum networks, quantum sensor networks, and distributed quantum computing. The center includes other quantum
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findings regularly. Prototype algorithms, validate outputs, and document methods clearly Collaborate asynchronously with an international team, present findings regularly Experience with remote sensing
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complementary satellite data (e.g. multispectral, multi-frequency SAR sensors) to improve the characterization of forest structure, biomass and deforestation dynamics at various scales. Development
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programming (Python, MATLAB), neural networks, deep learning or physiological signal analysis. - Minimum of 2 years of accredited professional or research experience in tasks directly related to: AI algorithm
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experience in Python, C++, MATLAB, or LabVIEW. Brief Description of Duties (Postdoctoral Associate): The selected candidates will join the Atmospheric Remote Sensing Group , a leader in innovative multi-sensor
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vehicle (AV), allowing for automated detection, prediction, mapping, and planning. During the vehicle’s operation, data is obtained through a myriad of sensors in an AV—including RADAR, LIDAR, cameras, and
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degree in informatics. Work plan: 1- Review of the literature on cooperative air transport and multi-agent control; select drone platforms and tools. 2- Evaluate and simulate sensor configurations for pose