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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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in applied economic analysis (e.g., causal inference, econometrics, spatial equilibrium modeling). • Experience working with large-scale datasets and interdisciplinary research. • Demonstrated research
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different types of social and ecological data at various spatial scales; o Experience working in R; o Strong written, digital and verbal communication skills, able to translate complex ideas into accessible
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/spatial multi-omics and epigenomic data in applications spanning development, cancer, neurodegeneration, and immunology. Publish in top venues, present at major conferences, and contribute to open-source
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, histopathology, mouse and human organoids, CRISPR-Cas9 gene editing, single-cell sequencing, and spatial transcriptomics. A PhD, MD, MD/PhD, or equivalent degree in biomedical sciences-related fields and a track