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(PROs) Neuroimaging and biomarker datasets Core methodological areas of interest include: Uncertainty quantification for individual-level predictions Methods for distribution shift (e.g., domain
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uncertainty quantification are encouraged to apply. The successful applicant demonstrates great attention to detail, works independently, takes initiative, has excellent written and verbal communication skills
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simulations and multiscale spatial-omics data. • Integrate uncertainty quantification into scientific machine learning workflows and optimize the design of computational (ABM) and wet-lab experiments
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uncertainty quantification into scientific machine learning workflows and optimize the design of computational (ABM) and wet-lab experiments. • Collaborate with mathematical modelers and experimentalists in