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Field
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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to the continent, and sub-daily to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including artificial intelligence (AI) and machine learning, to help
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or veterinary contexts. Strong foundation in computer vision and machine learning frameworks (e.g., PyTorch, TensorFlow, OpenCV); experience with video annotation tools and datasets. Hands-on experience with
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medical, dental and vision coverage effective on your very first day 2:1 Match on retirement savings Responsibilities* Researching and developing novel machine learning architectures for integration across
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from backgrounds, including computational chemistry, bioinformatics, systems biology, physics and machine learning. The project offers a unique opportunity to collaborate closely with experimental
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, Information, and Data Sciences (2 ) Environmental and Marine Sciences (7 ) Life Health and Medical Sciences (1 ) Science & Engineering-related (1 ) Veteran Status: Veterans Preference, degree received
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, systems biology, physics and machine learning. The project offers a unique opportunity to collaborate closely with experimental scientists and contribute to translational advances in synthetic biology and
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requirements: PhD degree in Computer Science, Electrical and Electronic Engineering, or related field. At least 3 years of relevant experience in computer vision, artificial intelligence, etc. Proficiency in
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Citizenship: U.S. Citizen Only Degree: Doctoral Degree. Discipline(s): Computer, Information, and Data Sciences (1 ) Earth and Geosciences (21 ) Environmental and Marine Sciences (14 ) Life Health and