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Field
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methods for analyzing complex experimental or imaging data. Demonstrated expertise in several of the following areas: inverse problems, statistics, optimization, uncertainty quantification, and/or computer
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been proposed: traditional computer‑algebra methods [3], reduction of the problem modulo a prime p [6, 2], and symbolic‑numeric methods [4, 1]. This postdoc proposal concerns the second modular approach
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). Experience in the application and development of computational methods/tools or machine learning algorithms. Good computer programming skills in R/Matlab/PerlPython. Knowledge of basic molecular biology
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Division (MSTD) at Oak Ridge National Laboratory (ORNL), and who will focus on ORNL's continued development of methods to quantify shear (yield) strength of monolithic ceramic materials as a function of
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areas : 1/ Development of methods to estimate residential environmental exposures • Develop spatialized indicators of exposure to atmospheric contaminants (gaseous pollutants and pesticides) based
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understanding of the interrelations between air quality, climate change, and human health, with expertise in atmospheric composition modeling, air quality forecasting, satellite-based PM2.5 estimation, and
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, optimization, and characterization integrating imaging, experimental metadata, and diffraction outcomes. Design and deploy computer vision methods to detect and track crystal growth. Develop closed-loop
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License/Registration/Certification n/a Physical Requirements Some standing or walking. Sitting at computer workstation for extended periods. Repetitive motion. Lifting, pushing, or pulling of objects up
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physics-informed and physics-ML hybrid approaches that integrate domain knowledge with data-driven methods to advance hydrological process understanding and prediction. Conduct multimodal, multiscale data
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already have used data-driven computational methods to model cognitive or behavioural change in any substantive domain, that would be ideal. Experience specifically with research on consumers or citizens