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analytics. The research assistant will develop real-time flight control algorithms, AI- based data processing, and mission management platforms, contributing to the deployment of smart UAV demonstrators under
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—specifically fine particulate matter, heat, and humidity—in Philadelphia neighborhoods. The fellow will extend existing research in AI- and data-driven modeling of building systems by developing algorithms and
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data are needed to enhance our understanding of sources, pathways and impact of litter. Cefas is developing a visible light (VL) deep learning (DL) algorithm and collected a large 89 litter category
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Student assistant (m/f/d)-Multi-Modal Sensor Fusion, Object Detection in Clutter and Object Tracking
an automated evaluation pipeline. Calibrate and time-synchronize multi-camera systems with tactile sensors; define data schemas and ROS 2/Isaac ROS interfaces. Algorithm Implementation: Develop multi
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, integrate, and validate sensors, especially but not limited to biomedical sensors for physiological monitoring. --Collect experimental data, implement algorithms and signal processing, contribute to system
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, novel algorithm development and battle management. Minimum Qualifications: 1) Bachelor's degree 2) 8+ years professional experience 3) Due to the requirements of our research contracts with the U.S
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, including Computational Fluid Dynamics (CFD) for thermal analysis and energy simulation for consumption modelling. Design, train, and implement advanced machine learning and deep learning algorithms
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University of North Carolina Wilmington | Wilmington, North Carolina | United States | about 20 hours ago
including clinical, algorithm, and mechanical engineering to define system requirements, sensor specifications, and patient interaction protocols. • Develop custom control and data processing software in
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Requisition Id 15490 Overview: The Multimodal Sensor Analytics group in the Electrification and Energy Infrastructure Division (EEID) is seeking a postdoctoral researcher with proven expertise in
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sensors, actuators, control algorithms, and embedded systems. Guide the development and deployment of digital twins for real-world processes and infrastructures (e.g., factories, greenhouses, robotics