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. This is a unique opportunity to serve as the founding member of a new lab at Stanford. The gut microbiota—dense, diverse microbes inhabiting the GI tract—drives host metabolism, xenobiotic processing, and
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for field and laboratory datasets. Perform statistical analysis and spatial modeling using R and/or Python. Develop machine learning models for digital soil mapping applications. Create and maintain
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to advance microbiome science, with a focus on the understudied small intestine, whose ecology is altered across many gastrointestinal disorders. The gut microbiota—dense, diverse microbes inhabiting the GI
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collaborators Experience working in participatory processes Experience in decision analysis and support processes Teaching experience Experience in geospatial modeling and GIS Experience in R, python, or another
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skills (python, r, github, GIS, optimization) ● Demonstrated ability to facilitate and model thoughtful interactions in a team environment with other experts including, professional staff in other IonE
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, remote sensing, machine learning Technical proficiency with quantitative data analytics and workflows (e.g. R, Python, GIS, GitHub) Experience with machine learning, Google Earth Engine, remote sensing
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Texas communities become more resilient over the longer term. To learn more, visit https://idrt.tamug.edu What We Want We seek a highly skilled and collaborative Data Scientist to lead the development and
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Qualifications: Proficiency in programming and data analysis tools (e.g., SQL, Python, R, GIS platforms), experience with integration of human subject data (e.g., questionnaires, interviews, eye-tracking
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expertise in forest ecology, disturbance ecology, and landscape ecology, and methodological expertise in harmonizing distinct databases (e.g., forest inventory, remote sensing, land cover), GIS, and R-based
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limited to: Conducting statistical analysis of PFAS in groundwater data. Performing spatial data analysis using orbital or airborne images and GIS tools. Reviewing and compiling relevant literature