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at the intersection of numerical analysis, uncertainty quantification, and scientific machine learning. The research will primarily focus on probabilistic methods for data-driven model reduction, with
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methods and sample-preparation strategies; (iii) interpretable multivariate or ML models for classification and feature discovery; and (iv) high-impact publications and open, reusable analysis workflows
Searches related to parallel computing numerical methods
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