74 machine-learning "https:" "https:" "https:" "https:" "RAEGE Az" positions at University of California Riverside in United States
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and apply advanced analytical frameworks--including geospatial statistics, machine learning tools, air quality modeling, and source apportionment techniques--to interpret air pollution observations and
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collaborative problem-solvers specializing in data science, statistical, and/or machine learning methods and tools, and have worked with various forms of data, including text. They should also enjoy conducting
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at the University of California, Riverside (https://www.physics.ucr.edu/ ) invites applications for a full-time tenure-track Assistant Professor position in Experimental Atomic, Molecular, and Optical Physics/Quantum
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physical environments. This position focuses on research at the intersection of computer graphics, generative AI, and robotics, encompassing topics such as generative modeling, reinforcement learning, multi
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georeferenced data on pest outbreaks. The Scholar will use these data in collaboration with computer scientists to develop machine learning algorithms for the detection and management of biotic stress. The crops
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at the time of application include: Applicants must possess a Ph.D. degree by date of employment. To apply, please submit the following materials to https://aprecruit.ucr.edu/apply/JPF02175: * Curriculum Vitae
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vitae (CV), and, optionally, up to three letters of reference. Applications must be submitted through AP Recruit at https://aprecruit.ucr.edu/JPF02201 . Full consideration will be given to applications
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Familiarity with UC/UCR policies and procedures. Knowledge of UCR accounting processes and financial systems To apply, please visit: https://irecruitportal.ucr.edu/irecruit/!Controller?action
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to https://aprecruit.ucr.edu/apply/JPF02172: * Curriculum Vitae (required) * Cover Letter (optional) * Contact information for three references (required) Review of applications will begin on February 5
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to published or in-press research in the field, professional recognition in the field, and a demonstrated record of University and/or public service. To apply, please submit the following materials to https