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), Coastal Hydrodynamic modeling (FVCOM), nitrogen routing models, data analysis and visualization, writing and modifying scientific code in Python and/or Fortran are required. A recent publication using these
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), Coastal Hydrodynamic modeling (FVCOM), nitrogen routing models, data analysis and visualization, writing and modifying scientific code in Python and/or Fortran are required. A recent publication using these
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-scale modeling using SWAT+ and APEX. Proficiency in programming languages such as Python and R for data processing. Background in applying AI/ML techniques to hydrology or water quality research. Strong
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-scale modeling using SWAT+ and APEX. Proficiency in programming languages such as Python and R for data processing. Background in applying AI/ML techniques to hydrology or water quality research. Strong
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be given to candidates with experience in eye-tracking methodology and analyses, programming knowledge, and experience using R, MATLAB, or Python for statistical analysis and collection. Candidates
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. Candidate should have experience in sensor technology, irrigation, and agricultural field research. Excellent statistics practical knowledge. Familiarity with programming languages such as Python, R, and Java
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. Candidates with strong and demonstrable programming language skills (e.g., Fortran, Python, SQL) are highly desirable. Desired Qualifications Experience in HYDRUS, SWAT, MODFLOW Integration, PFAS fate, and
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regression techniques. Strong quantitative and programming skills (e.g., R, Python, or MATLAB). Solid foundation in advanced mathematics, including topics such as real analysis, complex analysis, computational
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Qualifications Day to day, the expectation is that the researcher will be expected to learn or have prior experience with a programming language such as python, Unix-based operating systems such as MacOS or Linux
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structural modeling and estimation. Advanced knowledge in causal inference and use of econometric tools and statistical software (e.g., Stata, R, Python) to conduct research using secondary and primary data