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methods of data analytics (e.g., statistics, stochastic analysis, Bayesian statistical analysis), physically-based hydrology and water quality models, and the use of machine learning tools for modeling flow
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modeling. 80% research - The project focuses on developing theoretical models using optimization and information theory to improve understanding of plant hydraulic regulation at the leaf, plant, and
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skills in R programming - Working knowledge of Python - Experience with basic analyses to characterize gut microbiomes, including diversity analysis, differential abundance analysis, modeling microbial and
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our ongoing research, our department members contribute to the growing understanding of how students learn; through their teaching and outreach, we model the implementation of evidence-based educational
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, Molecular Biology, or a closely related biomedical field • Experience with retinal immunopathology, photoreceptor biology, or RPE-related degenerative disease models • Demonstrated expertise in retinal
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analysis of data including measures of pupil dilation, microsaccades, and behavioral measures of speech perception. Experience with data collection and statistical modeling of time-series data are essential
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validate polarizable force field models. Responsibilities will include both macroscopic property analysis (e.g., density, diffusion coefficients, ion conductivity, interfacial tension) and atomistic-level
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-activated immunogenicity, scaled-up vector production, and assessment of AAV efficacy in pre-clinical animal models. The goal of our research is to ensure advancement of gene therapy treatment for patients
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of innovative models that simulate the intestinal environment and study disease progression. Participate in peer-reviewed manuscripts in top scientific journals and contribute to scientific grant funding
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tissue culture, experimental virology, transcriptome analyses, and immunologic assays. Prior experience conducting relevant experiments using in vitro and in vivo models of infection, such as flow