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total of five years. This appointment is not intended for long-term, indefinite, or career appointments. I. POSITION INFORMATION Job Description Summary: The George Washington University Institute
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quantitative analysis; prepare manuscripts and grant applications; and present data and findings to internal and external audiences. Successful candidates will have the ability to collaborate with faculty, staff
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of physics beyond the Standard Model, using analytical and computational approaches. The successful applicant will join Dr. Carlos Blanco's research group with significant academic freedom to pursue
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in manufacturing energy use, process systems, or industrial emissions reduction technologies. Possess an excellent quantitative and analytical background acquired through PhD research, which can be
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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | 15 days ago
Earth eXchange (NEX), housed at NASA Ames Research Center in Silicon Valley, is a collaborative supercomputing and data analytics platform that improves the availability of Earth science data from NASA
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Position Details Position Information Recruitment/Posting Title Postdoctoral Associate Job Category Staff & Executive - Research (Laboratory/Non-Laboratory) Department RWJ-CABM Overview New Jersey’s
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research team. This position offers an exciting opportunity to contribute to cutting-edge research at the intersection of artificial intelligence reasoning and renewable energy analytics. Job
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University of California, Los Angeles | Los Angeles, California | United States | about 16 hours ago
demonstrated quantitative analytical abilities. Application Requirements Document requirements Curriculum Vitae - Your most recently updated C.V. Cover Letter Statement of Research Reference check
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within a few months), for example in economics, social science or related field Excellent mathematical and analytical skills from fields such as mechanism design and optimal taxation Familiarity
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). The emergence of data-driven techniques (broadly grouped under the term “machine learning”) challenges the traditional foundations of controls and represents an alternative paradigm that cannot be ignored