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focus on designing architectures and models that effectively capture the complexities of data, provide robust confidence estimates in predictions, and generate compressed quantities of interest tailored
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/models which accurately capture the complexities of the data, with robust estimates of confidence in predictions and compressed quantities of interest on defined domains; Fast and scalable algorithms
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Laboratory (ORNL) is seeking several qualified applicants for postdoctoral positions related to Computational Methods for Data Reduction. Topics include data compression and reconstruction, data movement
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in scientific research. Knowledge of design and analysis of complex systems using AI. Knowledge of data management, high performance I/O, and data compression methods. Experience working in a cross