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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
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, numerical methods, and Earth system modeling to develop and evaluate a coupled xylem–phloem transport framework that translates multiscale physics into next-generation vegetation model schemes. Key
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) Experimental investigation and computational model simulation of laser-induced bubble dynamics and material damage assessment 3) Developing AI and machine learning models for robot-assisted laser surgery and
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for genomics (e.g., generative models, transformers, agentic workflows) and/or statistical learning (e.g., network & spatiotemporal modeling, functional/longitudinal data, time-series). Analyze single-cell
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related field • Strong quantitative background (e.g. ecological theory and mathematical modeling, hierarchical statistical modeling, machine learning, remote sensing, geospatial statistics) • Demonstrated