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challenges facing the nation. We are seeking a Research Scientist who will focus on the applications of Artificial Intelligence (AI) and Machine Learning (ML) in Urban Systems. This position resides in
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-destructive testing, machine or process monitoring, or similar applications of algorithmic tools. Demonstrated expertise in the adoption of modern machine learning tools for time series analysis Preferred
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reinforcement learning and machine vision. Experience with ROS and the ROS ecosystem Special Requirements: Applicants cannot have received their PhD more than five years prior to the date of application and must
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pertains to nuclear fuel cycle. Demonstrated experience in Python, MATLAB, R, or similar. Demonstrated experience with applying techniques to introduce uncertainty quantification to machine learning
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industrial automation, machine learning, mobile robotics, process control, sensor processing, machine vision, and/or human machine interaction. This position will require working with external partners
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science, computer engineering, information technology, information systems, science, engineering, business, or a related discipline and a minimum of eight (8) to twelve (12) years of aligned professional experience
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related activities at ORNL.Qualified applicants will have a solid foundation of Generative AI and Machine Learning skills. This position resides in the AI Operations Program office within the Application
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. Proficiency with ROS, Python, and MATALB, as well as with corresponding AI/machine learning libraries. Experience designing and assembling hardware and software systems for building construction applications
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journals and conferences. This role provides a unique opportunity to work with the world’s first exascale system, Frontier, and collaborate with leading experts in machine learning, optimization, electric
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materials such as the magnetoelectric, high entropy oxides, through neutron scattering experiments. Additionally, collaborative work will be performed with the aim of developing and applying machine learning