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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
Posting Information Posting Details Department Physics and Astronomy - 319000 Posting Open Date 09/25/2026 Application Deadline 03/23/2027 Open Until Filled No Position Type Postdoctoral Scholar
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Systems and Software Program (WASP ). You can find more information about us on the Department of Information Technology website. The position is hosted by the Division of Scientific Computing (TDB), one
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for this position have a great interest and knowledge in data analysis, filtering and modeling based on large amounts of data. It is important that you have a solid understanding of mining and mining systems to also
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conducts theoretical, methodological and applied research, with emphasis on statistical methodology for large-scale and register-based data and on applications of societal relevance. The successful candidate
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; familiarity with time series analysis and complex survey design and weighting is preferred. Experience in epidemiological data analysis of large datasets using R and/or STATA is highly preferred, competency in
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. The role includes implementing developed techniques and algorithms in high-quality software that adheres to group standards and evaluating them on large-scale benchmarks. You will take an active role in
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ice, using a framework consisting of an ocean Large Eddy Simulation (LES) and a Discrete Element Model (DEM) of sea ice. Results from these simulations will be validated against a combination of in-situ
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, the deadliest malaria parasite species. Within UniGEM, the postdoc will be responsible for the simulation of synthetic data used to train, develop, and test the neural network. Scientific context: Malaria
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await you. Experience all that a big city has to offer but with a reasonable cost-of-living and no long commutes. Medical, prescription drug, dental, vision, life and AD&D, flexible spending
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for flexibility in the start and end dates, subject to approval by the department and the funding agencies. The successful candidate will join the Machine Learning & Data Science research group and conduct research