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background in machine learning, predictive modeling, or applied AI Proficiency in Python and/or R; experience with libraries like scikit-learn, XGBoost, TensorFlow. -Experience working with real-world datasets
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Conservation Biology, or equivalent degree Preferred Education: Ph.D. in Ecology and Conservation Biology, Preferred Experience: Experience with R programming, statistical analysis of coastal datasets
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in transcriptomics, and basic molecular techniques such as DNA/RNA isolation, PCR and qPCR and statistical analysis using R or python based packages would be preferred. The position is available
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in genomics, genetics, and statistical analyses. Experience with transcriptomic and epigenomic data analysis. Proficiency in programming languages, such as R and Python, for data analysis and
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(GAM) and statistical and geospatial software (R, ArcGIS, QGIS) is strongly preferred. What You Need to Know Salary: Compensation for this position is commensurate based on the selected candidate’s
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with grant writing Excellent data analysis skills in R. What You Need to Know Salary: Compensation for this position is commensurate based on the selected candidate’s qualifications. Position Funding
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proficiency in languages such as R and Python. Experience in GIS, remote sensing, and processing projected climate data. Proven ability to manage multiple tasks effectively, work collaboratively in team
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statistical and geospatial software (R, ArcGIS, QGIS) and atmospheric dispersion modeling (AERMOD, WindTrax, CALPUFF) is strongly preferred. Ability to multi-task and work cooperatively with others. Why Work
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, R, XLstat, Multivariate analysis). Proficiency in Microsoft Office (Excel, Word, Outlook). Willingness to work evening and weekend hours as required. Preferred Knowledge, Skills, and Abilities
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biochemistry techniques in weed science is essential Ability to use R statistical program or python is required Expectation of experience in computational tools and unix for analyzing and visualizing large data