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Requisition Id 15553 Overview: We are seeking a Postdoctoral researcher in data quality assessment and control and sensor network optimization who will focus on R&D of sensor network design. This
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place technologies and to develop digital twin algorithms to assist clinicians in developing treatment plans for patients with chronic diseases. Analyzes complex sensor data, works with a
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implement sensor fusion and SLAM (Simultaneous Localization and Mapping) algorithms, integrating data from sources like GPS, IMU, and LiDAR to achieve robust vehicle localization. Implement, test, and tune
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, and autonomy. You will join cross-disciplinary research teams working on prototype systems that integrate acoustic modems, sensor arrays, signal processing algorithms, and AI-enabled control
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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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. The postdoc will develop machine learning algorithms to analyze phenotype and sequence data, as well as active learning algorithms to optimize and control experiments in directed evolution. This position
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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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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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research team. Key research areas include: Development of low-carbon materials and tunable thermal energy storage materials integrated with smart sensors and advanced algorithms Creation of Digital Twins
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control hardware. Collaborate with cross-functional teams including clinical, algorithm, and mechanical engineering to define system requirements, sensor specifications, and patient interaction protocols