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Field
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the possibility of yearly renewal subject to funding availability. Key Responsibilities • Conduct and lead research in 3D computer vision, deep learning, and AI for digital twin generation, publish findings in top
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outcomes ●casual representation learning for real-world data ● deep learning interpretation, fairness and robustness ●Regularly conduct computational experiments to execute algorithms on various health and
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: 6493962 Postdoctoral Associate, Center for Climate Change and Health Equity Position Information Position Title: Postdoctoral Associate, Center for Climate Change and Health Equity Department: Epidemiology
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. Current robotic systems rely primarily on vision and force sensors, missing the rich tactile information that enables human dexterity. Our lab pioneered a new tactile sensor technology (best student paper
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applications where robots must interact delicately with the physical world. Current robotic systems rely primarily on vision and force sensors, missing the rich tactile information that enables human dexterity
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expertise in analysing/ training models on biological or chemical datasets Proficiency in Python for data science and machine learning Possess sufficient breadth or depth of specialist knowledge with deep
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of biosystems and for extracting knowledge from (vast) sets of biotech data. A core technology leveraged by researchers at the center is deep machine learning, targeting the development of innovative tools and
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exceptional postdoctoral research fellows interested in developing deep learning and computational methods for pathology image analysis, multimodal data integration, and other medical modalities (e.g
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research approaches. Working closely with nursing partners and clinical collaborators, our exciting work combines non-invasive imaging technologies, deep learning, computer vision, and clinical workflow
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Essentials PhD (completed or near completion) in Computer Science, Computer Vision, NLP, Machine Learning, Computer Graphics/Animation, HCI, or a related field. Strong background in deep generative