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scenarios. The researcher will develop profit–risk–environment tradeoff products, build reusable R/Python workflows, and publish and present results in collaboration with USDA-ARS and university partners
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cooperatively with others. Preferred Qualifications: • Proficiency in handling remotely sensed geospatial datasets and programming languages, particularly R or Python. • Knowledge, experience, and
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, Python, GitHub, fieldwork, laboratory workflows, or grant/manuscript development. Experience with R, Git/GitHub, phylogenomic or population genomic workflows, GIS/spatial analysis, herbarium curation
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. Responsibilities: Conduct empirical research on responses to hazardous weather using mobility data Clean, manage, and analyze large mobility datasets using python and modern econometric and computational tools
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, transcriptomics, metabolomics, phenomics, microbiome) for predictive modeling and biological interpretation. •Proficiency in Python, R, AI/ML frameworks, and bioinformatics pipelines for high-throughput data
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, or livestock waste systems. Experience with data analysis, modeling (e. g., ammonia emissions or air quality models), and statistical software (R, SAS, Python, etc.). Demonstrated ability to conduct independent
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of hydrologic processes, transboundary/shared water resources management, and coupled human–natural systems. Proficiency in geospatial and scientific computing tools (e.g., Python, R, GIS, Google Earth Engine
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annotation, comparative or population genomics, command-line work in a Linux/HPC environment, and scripting in at least one of Python, R, or Bash. Hands-on molecular biology (DNA extraction, library
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• Bioinformatics and data analysis using R and Python • AI/ML-assisted biological data analysis. • Expertise in assay development for new compounds and ingredients. • Ability to manage laboratory personnel
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. Proficiency in scientific programming (e.g., Python, Fortran) for data processing, model development, and computational analysis. What You Need to Know Salary: Compensation for this position is commensurate