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-up solutions for different management scenarios. The researcher will develop profit–risk–environment tradeoff products, build reusable R/Python workflows, and publish and present results in
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. •Proficiency in Python, R, AI/ML frameworks, and bioinformatics pipelines for high-throughput data analysis and decision support in plant breeding. •Experience developing AI-enabled decision support tools
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in R or Python. Plan and support environmental sampling in production settings, including coordination collaborators. Maintain careful records and contribute to laboratory data management practices
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. • Ability to multi-task and work cooperatively with others. Preferred Qualifications: • Proficiency in handling remotely sensed geospatial datasets and programming languages, particularly R or Python
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/Poaceae, trait datasets, biodiversity informatics, R, Python, GitHub, fieldwork, laboratory workflows, or grant/manuscript development. Experience with R, Git/GitHub, phylogenomic or population
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EPIC, APEX, RUFAS, BCSM, GLEAM, or similar simulation frameworks. Proficiency in scientific programming (e.g., Python, Fortran) for data processing, model development, and computational analysis. What
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, metagenomics, RNA-seq and transcriptomics analysis • Multi-omics integration • Bioinformatics and data analysis using R and Python • AI/ML-assisted biological data analysis. • Expertise in assay
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, Python, etc.). Demonstrated ability to conduct independent and collaborative field and laboratory research. Excellent written and oral communication skills with a track record of scientific writing
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geospatial and scientific computing tools (e.g., Python, R, GIS, Google Earth Engine, or equivalent) to handle and process complex datasets. Excellent written and verbal communication skills, including the
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scripting in at least one of Python, R, or Bash. Hands-on molecular biology (DNA extraction, library preparation, PCR/qPCR). Experience handling Oxford Nanopore (ONT) sequence data. A record of scientific