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biologically-constrained machine learning–based model discovery pipelines to derive interpretable surrogate ODE/PDE models from simulated ABM data and spatial-omics data collected from state-of-the-art
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managing human subjects research and handling sensitive data is preferred. • Strong quantitative skills, including proficiency in regression modeling, environmental mixtures analysis, and spatial methods
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model over the Contiguous United States, and evaluate model deficiencies and model improvements to improve the modeling of spatial heterogeneity of LST in land surface models. In Addition, Will Also
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cultured human and rodent cardiomyocytes, engineered heart tissues, and animal models of heart development and disease. Specifically, you will engage in basic science and applied research to explore
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will develop novel statistical and machine learning methods for any of the following: multi-omics data (such as bulk and large-scale single-cell RNA sequencing data, spatial transcriptomics, bulk and