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Field
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issues. The ideal candidate will have a strong background in data analytics, be proficient in a general programming language (like Python) or a statistical package (R), and an appetite for engaging in non
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environmental modeling, including groundwater mapping and evapotranspiration projection. Strong skills in GIS, spatial analysis, and programming languages such as Python, and MATLAB, Res2dinv/mod, Res3dinv/mod
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education above and beyond what is required may be substituted on a year-for-year basis. Skills: Proficiency in Tableau and/or PowerBI Knowledge of Python or R Experience with Census data, shapefiles
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in analytical tools (e.g., MATLAB, Python, GIS, or similar). Knowledge of AI and Multi-Agent Systems. Proven ability to analyse complex information and summarise appropriately Ability to demonstrate
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tools from remote sensing, Geographic Information Science (GIS), graph theory, and data science to address complex research questions. Analyzes both aspatial (e.g., tabular) and spatial (vector and raster
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the developed geospatial databases to evaluate trends in stream networks across the US. Work will be done primarily in python and GIS. Candidates will be expected to create maps and graphs of trends in network
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: hydrogeology and groundwater modeling Technical proficiency with quantitative data analytics and workflows (e.g. R, Python, GIS, Modflow, GitHub) Experience conducting interdisciplinary or community-engaged
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quality analysis, big data analytics, isotopic analysis Technical proficiency with quantitative data analytics and workflows (e.g. R, Python, GIS, Modflow, GitHub) Experience conducting interdisciplinary
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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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The School of Natural Sciences seeks to recruit an enthusiastic technician with strong GIS knowledge and spatial data analysis skills, with additional experience of working in the field and