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focuses on the rigorous statistical and probabilistic foundations of machine learning and data science. We emphasize computational methods for large-scale data and scalable inference techniques. Our current
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. Kyriakopoulos seeks to improve the autonomy of Field Robotic systems by fusing control theoretic and machine intelligence approaches. Formal models are directly applied in real experimental facilities. Marine
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, and Simultaneous Localization and Mapping (SLAM) is desired. The position is open to PhDs with background in robotics, controls, AI, and/or computer vision. The candidate is expected to work in a highly
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. The Department of Robotics focus is on rigorous, high-impact, original research emphasizing robot learning, (eg CoRL) and robot algorithms (eg WAFR) rather than development of new robot hardware. Research topics
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Postdoctoral Associate to work on a fascinating project focused on the development machine-learning powered digital twin system for the structural performance of civil engineering structures. The project is a
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must have a PhD in Robotics, Control Theory, Mechanical or Electrical Engineering, Applied Mathematics, or a closely related field, with a strong focus on robot control, machine learning, and
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development (R&D) of advanced machine learning (ML) models like Transformers, Vision Transformers, Large Language Models (LLMs) and other advanced deep learning models for vision-based applications. The focus
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, statistical signal processing, optimization theory, machine learning and artificial intelligence. The candidate is expected to actively participate in experimental work focused on building datasets of channel
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Postdoctoral Associate to advance cutting-edge research in machine learning (ML). Our lab explores the intersection of artificial intelligence, and human-computer interaction, striving to create technologies
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. Applicants must have a PhD in mechanical engineering, and experience with computational heat transfer and machine learning for heat transfer, especially radiation. For consideration, applicants need to submit