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, data analysis, and visualization environments/IDEs (e.g., R/R Studio, Python/Spyder/VSCode) Familiarity with GIS methods and cloud-based computing resources (e.g., Google Earth Engine) for large-scale
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integrating geophysical data with groundwater or vadose zone models Familiarity with irrigation systems and agricultural water management Programming experience (e.g., Python, MATLAB, R) for data analysis and
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with bioinformatic analysis of high-throughput sequencing data Proficiency in R, Python, or other relevant computational tools Evidence of scholarly productivity (peer-reviewed publications) Excellent
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Earned Ph.D. in Biosystems Engineering, Food Science, or equivalent Demonstrated expertise in computational modeling Demonstrated experience coding in multiple programming languages (R, Python, MATLAB
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. Significant experience using Python for high performance applications with libraries such as: Numpy, Numba, CuPy, and PyQtGraph Significant experience using C++ for numerical computing applications
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downloading, organizing, documenting, quality-controlling, analyzing, and interpreting time-series or spatial monitoring data using Python, R, MATLAB, or comparable analytical tools Excellent written and
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software(s), proficient in programming in both R and/or Python. Ability to collaborate and clearly communicate with stakeholders, state and federal agency biologists, university scientists, and non