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Requisition Id 14798 Overview: As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an impressive 80-year legacy of addressing the nation’s most pressing challenges. Our team is made up of over 7,000 dedicated and innovative individuals! Our goal is to create an...
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not required as part of this position. Required Qualifications: Strong mathematical background, including expertise in one or more of the following areas: machine learning, statistics, and algorithms
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: Candidate must have a strong quantitative background, with a PhD in computational biology, bioinformatics or related field including bioengineering, computer science, statistics, or mathematics. Strong
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, mathematics, physics, or a related field. The ideal candidate should demonstrate a record of publications in the area. Strong knowledge in machine learning, statistics and programming skills (R, Python
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, applied mathematics and computational sciences with a strong focus on computational humanities research. The project leverages an extensive dataset of digitized books, newspapers, and visual artworks
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We are no longer accepting applications for this recruitment. Browse open recruitments Position overview Position title: Postdoctoral Scholar in Fast Sensors Group Salary range: Commensurate with qualifications and experience. The posted UCSC salary scales set the minimum pay based on the...
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. Minimum Requirements • PhD in a quantitative field, including physics, neuroscience, mathematics, statistics, computer science, or related fields. • Strong quantitative background including at least some
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, mathematics, statistics, documentation, records management, data utilization, complex problem solving, critical thinking, resource management, and writing skills. Skills and abilities: This will vary based
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must have taken relevant higher education pedagogical courses. Requirements: Doctorate in computer science, mathematics, or similar. Strong command of both written and spoken English language
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Education/Experience A PhD in natural resource economics, mathematics, geography, environmental studies, natural resource management, or similar disciplines Experience and/or training with rules-based models