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policy requires that after obtaining a PhD, eligible candidates for research associate appointments may not exceed a combined total of 5 years of relevant work experience as a post-doc and/or in an R&D
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 months ago
Posting Information Posting Information Department Psychiatry - Research - 412250 Posting Open Date 01/21/2026 Application Deadline 02/04/2026 Open Until Filled No Position Type Permanent Faculty
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Science, Computational Biology, Bioinformatics, or a closely related field. Experience with computational modeling, statistical programming (Python, R, MATLAB), or machine learning. Experience working with large datasets
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to numerical methods for kinetic equations. Mathematical topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and
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to Computational Methods for Data Reduction. Topics include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a
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statistics and in general data science. You - the Post doc - is particularly expected to do research work within civil engineering with a focus on sustainability. Additionally, you will have the opportunity
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Post-Doc Fellow, Causal Inference - 38493 Faculty Description University of Colorado Anschutz Department: Medicine, Division of General Internal Medicine Job Title: Post-Doc Fellow, Causal Inference
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Current Employees: If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how
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) with questions related to this position. Major Duties/Responsibilities: Develop and apply machine learning models (ML) as surrogates for high-resolution process-based hydrologic models. Design and
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Chekouo and his collaborators within and outside the University of Minnesota. The research will focus on the development of Bayesian statistical/machine learning methods for the data integration analysis