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-based assays to connect molecular structure and composition with function. This interdisciplinary framework enables research spanning fundamental chemical discovery, bacterial physiology and biofilms
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and Geographic Medicine, Stanford School of Medicine, with co-advising by Dr. Mathew Kiang, Assistant Professor of Epidemiology. The project involves developing a rigorous causal framework for assessing
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systems as a public good. As the program advances, it will refine its theoretical frameworks, pursue funded research across this portfolio, and continue to build a transdisciplinary community committed to
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interactions with GPCR IDRs across the broader GPCR superfamily. Within this collaborative framework, the postdoc will have the intellectual freedom and support to shape their own research direction leveraging
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. Leading the analysis of single-cell and spatial transcriptomics data. Applying and developing the analysis framework for spatiotemporal modeling. Publication in top-tier journals, and apply and obtain
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primates or humans – Theoretical neuroscience, machine learning, or AI • Proficiency in Python, MATLAB, or equivalent data‑analysis frameworks. • A passion for big‑picture questions, open science, and
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profiling. More broadly, this work aims to establish a new experimental framework for spatial multi-omic analysis that complements existing spatial transcriptomics technologies. Responsibilities
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; publications in medical imaging or health data science are highly desirable. Proficiency in Python; experience with ML frameworks (PyTorch, TensorFlow); familiarity with imaging libraries, data governance, and