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a particular focus on the integration of artificial intelligence with wireless sensing and communication. The research will study how radio signals and multimodal sensor observations can be jointly
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-language-action models, imitation and reinforcement learning, world models, multimodal perception, model compression and edge inference. A key aim is enabling robots to improve beyond initial demonstrations
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on AI-assisted reverse engineering of integrated circuits for hardware assurance and intelligence analysis. The project is conducted within the Deep Learning for Perception and Data Science, Safety and
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scientific initiative focused on AI-assisted reverse engineering of integrated circuits for hardware assurance and intelligence analysis. The project is conducted within the Deep Learning for Perception and
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that govern real-world phenomena, thereby providing a foundation for context-aware perception, reasoning, and decision-making. The research aims also to address the limitations of existing single-source sensing
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addresses that challenge by developing a multimodal sensing and inference framework that can run on compact AI edge hardware while remaining reliable in complex, contested, or visually degraded environments
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planning, control, and perception methods for aerial robotic manipulators (ARMs). These aerial manipulators will act as “flying hands” to support the connection and assembly of prefabricated building
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(e.g., conformal prediction, risk-aware decisions); and robotics for fabs?AMR planning/tasking, robotic vision & multimodal perception, wafer/FOUP/tool handling, and RL/Sim2Real. The successful candidate