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Field
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Systems program integrates the study of wireless networking, protocols, sensor networks, security, software development, embedded systems, data analytics, and big data to provide students with the knowledge
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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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, 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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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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of synoptic processes can be isolated from those driven by surface conditions. Thanks to advances in ground-based remote sensing technology and algorithm development, those profile observations can now be
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our industrial partners. You will work in the cyber analytics and CISE labs in the Algorithmics and Software Engineering Research groups at the Software Technology department under supervision of dr
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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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with the rest of the team, you will build demonstators for the Find2Fix technology at our industrial partners. You will work in the cyber analytics and CISE labs in the Algorithmics and Software Engineering
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operation. Develop modular architectures for multi-agent coordination, sensing, and communication. Integrate sensor suites, flight controllers, and swarm coordination algorithms into UAV platforms. Conduct
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-making algorithm for autonomous vehicles. The work will involve sensor fusion, perception, trajectory prediction and test rig set-up, and experimental validation. Job Requirements: PhD Degree in Vehicle