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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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, focusing on intelligent sensor tasking and the automated identification and characterization of space objects in Earth orbits and cislunar environment using optical data. Contribute to the development
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for SDA, focusing on intelligent sensor tasking and the automated identification and characterization of space objects in Earth orbits and cislunar environment using optical data. Contribute
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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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the development of image/signal processing algorithms from a multidisciplinary approach, to include multiple sensor modalities. These multidisciplinary research opportunities incorporate theoretical and
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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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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
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that will focus on applying robotic control and motion planning algorithms to real hardware to simulate multibody 0g space dynamics. Candidates with demonstrated experience with robotic algorithms and writing
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National Laboratory (ORNL). The NEEM Group focuses on pushing the limitations of materials, sensors, and experiments through technology development, advanced instrumentation, irradiation experiments, and
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troubleshooting to ensure real-world readiness. Develop and implement algorithms for autonomous control, SLAM and navigation. Ensure reliable communication between hardware components and the robotic system