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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 30 days ago
: 304827 Vacancy ID: PDS004622 Position Summary/Description: The position will involve substantial analysis of remotely sensed imagery, including data from SWOT itself and possibly also from other sensors
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Your Job: This thesis focuses on designing, evaluating, and deploying algorithms for robot perception and control. The main task is predicting both self-motion and the motion of surrounding agents
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, modelling and simulation of photonic systems, sensor systems, signal processing and device manufacturing, development of machine learning algorithms, and design of optical communication networks or power
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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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advances in 3D vision, AI-driven detection, and multimodal sensor integration (RGB, RGB-D, LiDAR), and digital twins. The postdoctoral fellow reports to Dr. Vedhus Hoskere in the College of Engineering/Civil
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sensors and DAS, acquired at sampling rates ranging from 1,000 to 200,000 samples per second, is capable of observing magnitude negative five induced earthquakes at distances from meters to 100’s of meters
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are searching for motivated and talented Signal Processing Research and Development Engineers to join our Sensor Analysis and Data Modeling (SADM) Department at the Applied Research Laboratory (ARL) at Penn State
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algorithms from concept to implementation; and performing systems integration and testing for technology maturation. Minimum Qualifications: 1. Ph.D. in relevant technical field 2. 6 years experience in
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quantum sensing technologies (e.g., Rydberg atomic sensors) for wireless communications and sensing. Key Responsibilities: Develop quantum-related theories, models, and algorithms for various communications
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Language Processing (NLP). We seek outstanding candidates with expertise in developing and applying AI techniques that integrate and reason over multiple modalities—including text, speech, vision, and sensor data