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Sensor AI team at imec, we build nextgeneration edgedeployed, multisensor perception systems designed to operate under strict size, weight, and power and real-time compute constraints. These systems merge
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devices—such as wearable sensors, assistive robotics, or implantable systems—where real-time performance, energy efficiency, and reliability are critical. Unlike traditional NAS approaches that are hardware
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and implementing computer vision and perception pipelines for challenging real-world, multimodal sensor environments, supported by strong expertise in ML, DL and AI algorithms. Proficient in training
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autonomy, we need teamwork: nearby vehicles, drones, and roadside units must co-perceive their environment, sharing and fusing complementary sensor views in real time. Yet raw video and point-cloud streams
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models using experimental data from drivetrain test benches equipped with load, temperature, vibration, and acoustic sensors. Integrating the models into a Model-Based Systems Engineering (MBSE) framework
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aerial vehicle (UAV) technology make them a promising solution to densify ELA monitoring. However, expanding the density of meteorological sensors in the ELA to bridge the spatiotemporal meteorological
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machine learning. This encompasses application domains such as industrial inspection, (ultra) high-definition video enhancement, smart multi-camera networks, computer vision, sensor fusion, and (medical