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U.S. Department of Energy (DOE) | Washington, District of Columbia | United States | about 2 hours ago
, Technology and Policy Program (DOE-STP) is designed to provide opportunities for students, postgraduates, and faculty to participate in programs, projects, and activities at the Department. Fellows will
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, computer science, artificial intelligence, or a related quantitative field Experience with mathematical/statistical modeling and/or machine learning/Al methods Strong interest in infectious disease epidemiology
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in R, Mplus, AMOS or other latent variable modeling software program Programming: R Population health or health systems Doctoral degree: A doctoral in social sciences (e.g. sociology, psychology
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Job Description Required Qualifications: (as evidenced by an attached resume) Ph.D. (or foreign equivalent) in Electrical Engineering, Computer Science, Applied Mathematics, or a closely related field
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research and teaching in applied analysis, computational mathematics, data science, discrete mathematics, financial mathematics, stochastics, and statistics. The Department of Applied Mathematics is the only
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workplace – in how we treat one another, work together, and measure success. Basic Qualifications: Ph.D. in computational data sciences and engineering, applied mathematics, geography, statistics, computer
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Sciences | Microbiology Administration The postdoctoral scholar is expected to lead computational/theoretical work in the area of environmental microbiology, and will have the opportunity to contribute
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) mathematics, physics, or a related STEM fields. Job Description: The Division of Biomedical Informatics and Data Science (BIDS) within the Department of Health Outcomes and Biomedical Informatics (HOBI
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 9 days ago
to be involved in experiments in the Sheikh lab. Minimum Education and Experience Requirements PhD in Computational Biology, Bioinformatics, or a related field. Required Qualifications, Competencies, and
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mathematics and engineering. The Interpretable Machine Learning Lab has dedicated access to high-performance CPU and GPU computing resources provided by Duke University’s Research Computing unit and state