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Field
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from buildings, mobile network data) Database management skills (e.g., PostgreSQL) Statistical expertise related to big data processing and high-performance computing (Python, R) GIS software proficiency
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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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society are required. Knowledge and skills of GIS and spatial studies are considered advantageous. To apply, please submit your application at https://careers.purdue.edu and include the following materials
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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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proficiency in languages such as R and Python. Experience in GIS, remote sensing, and processing projected climate data. Proven ability to manage multiple tasks effectively, work collaboratively in team
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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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support PI’s research and education activities to enhance environmental, soil, water quality, microclimate monitoring, Geographic Information System (GIS), and remote sensing research programs as part of
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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
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Strong organizational and time management skills Strong quantitative data analysis skills Experience using administrative child welfare data Experience and skills in GIS Grant-writing experience Additional