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techniques, by powder diffraction, PDF analysis, GI-PDF, and complementary characterization techniques, e.g. IR Experience in material synthesis Motivated and creative approach to research with the ability
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within the past 3 years in Environmental Science, Geography, Computer Science, Atmospheric Sciences, or related field. Advanced GIS and geospatial computing expertise. Demonstrated commitment and ability
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following topics: in situ sensor installation and flood monitoring, GIS & geospatial big data, AI/ML and data science approaches for hydrologic predictions, risk analysis. Experience in working with big data
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knowledge of social surveys and GIS are considered advantageous. To apply, please submit your application at https://careers.purdue.edu and include the following materials: (1) letter of application that
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knowledge of social surveys and GIS are considered advantageous. To apply, please submit your application at https://careers.purdue.edu and include the following materials: (1) letter of application that
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in spatial equilibrium modeling (e.g., through coursework, dissertation work, or previous projects) experience with spatial data analysis using GIS tools or spatial libraries and familiarity with
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, computer science, environmental engineering or in a relevant science subject area with an affinity for renewable energy integration, spatial analysis/GIS and programming; has proven experience in programming
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quantitative methods, including R and GIS. • Knowledge of terrestrial ecosystems and disturbance regimes. Preferred Qualifications • Experience with process-based models. • Knowledge of shrub or chaparral
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familiarity with model coupling frameworks (e.g., ESMF). Proficiency in programming and data analysis (e.g., Python, Fortran) and handling large datasets, including GIS or remote sensing integration. Strong
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students and contributing to educational initiatives. Experience with remote sensing, GIS tools, and image analysis techniques is an advantage, as is knowledge of genetic methods (e.g., SNP-based data