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research at the intersection of computing and healthcare. Methodologies of interest include: multi-modal learning, foundation models, including large language models, agentic AI, multi-agent AI
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research at the intersection of computing and healthcare. Methodologies of interest include: multi-modal learning, foundation models, including large language models, agentic AI, multi-agent AI
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safety, cooperation, and efficiency in human-robot teams. Multi-Modal ML: Expertise in working with diverse data types, such as vision, speech, images, and physiological signals. Experience integrating
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robotics/controls, and with less than 5 years of experience after receiving their PhD. The candidate is expected to work in a highly collaborative multi-disciplinary environment with other post-doctoral
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robotics/controls, and with less than 5 years of experience after receiving their PhD. The candidate is expected to work in a highly collaborative multi-disciplinary environment with other post-doctoral
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robotics/controls, and with less than 5 years of experience after receiving their PhD. The candidate is expected to work in a highly collaborative multi-disciplinary environment with other post-doctoral
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multi-scale experimental testing at the material, component, and structural levels. The candidates will work in a multidisciplinary environment consisting of PhD-level scientists and undergraduate
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complex diseases. The successful candidate will lead and contribute to large-scale population genomic analyses, with a focus on whole-genome sequencing and multi-omics data and the genetic
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for the control, optimization, and coordination of complex dynamical systems. The work will emphasize applications to multi-agent systems, including robotics, autonomous systems, and distributed
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complex diseases. The successful candidate will lead and contribute to large-scale population genomic analyses, with a focus on whole-genome sequencing and multi-omics data and the genetic