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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 4 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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Centre for Advanced Robotics Technology Innovation (CARTIN) is looking for a candidate to join them as a Research Fellow. Key Responsibilities: Develop novel algorithms for multi-agent inverse
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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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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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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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system automation. Investigate and improve human-machine interaction frameworks for construction operations, including robotics, sensor-based communication, and real-time monitoring. Publish research
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university/spinout environment. This is a unique opportunity to work at the forefront of applied research and innovation, helping translate novel control algorithms and hardware prototypes into real-world
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pipelines, Mixed Reality Toolkit customization, and performance tuning under hardware constraints; collaborating on robot motion planning, path optimization, and sensor data processing algorithms
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on complex system subject to various constraints. Design and develop real-time algorithms. Simulate and evaluate system performance. Document research outcome to publish at international conference / journal
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 17 hours ago
will involve substantial analysis of remotely sensed imagery, including data from SWOT itself and possibly also from other sensors including optical sensors (like Landsat) and other radar sensors. It may