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Field
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testing experience can be valuable. Statistics and Mathematics: Machine learning (supervised and unsupervised) methodology and evaluation including discrimination vs. calibration measures and (hyper
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genomics, metabolomics, or microbiome analysis Computer science, particularly machine learning, artificial intelligence, data science, or computational biology Mathematics or statistics, with experience in
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engineering. Classes and student activities are conducted in English. Applicants are welcome and encouraged from relevant engineering disciplines, computer science, mathematics, and physics. The institute has
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integrity in the design process calls for a technical model of the product concept, i.e., mathematical models of the product and its in-service environment, which inevitably must incorporate the influence
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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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fields include (but are not limited to) Computer Science, statistics, mathematics, automation, informatics, and Engineering. • Experience in deep learning, machine learning and medical imaging processing
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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