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– 10% Requirements Ph.D. in Atmospheric Sciences, Climate Science, or related field. Proficiency in scientific programming (Python, R, MATLAB, or equivalent) is required. Experience with atmospheric
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environmental impacts (e.g., greenhouse gas emissions and impacts to air and water quality). Interest in spatially explicit environmental impact assessments at regional and local scales. Proficiency in R, Python
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experience with deep learning (DL) methods. Demonstrated proficiency in Python and machine learning frameworks (e.g., PyTorch, Jax, scikit-learn) applied to genomic/related datasets. Experience with sequence
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analysis of biological datasets using R, Python, SAS, or related platforms Ability to integrate laboratory, greenhouse, and environmental datasets to support predictive analyses and IPM-focused decision
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-applied compounds • Experience with statistical modeling, experimental design, and multivariate analysis of biological datasets using R, Python, SAS, or related platforms • Ability to integrate laboratory
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-determination cascade. Proficiency in command-line bioinformatics and a scripting language for genomic data analysis (R, Python, or equivalent). Track record of first-author peer-reviewed publication (published
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, splicing variant identification, or functional dissection of the sex-determination cascade. • Proficiency in command-line bioinformatics and a scripting language for genomic data analysis (R, Python
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, MetaPhlAn, or similar). Proficiency in programming languages for data analysis (e.g., R, Python). Experience with mass spectrometry-based metabolomics approaches, including LC-MS/MS. Experience operating
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(e.g., Python, SPEC, MATLAB, etc.) Experience with relevant sample preparation and lab-based analyses (e.g., SEM, Raman) Experience with or interest in engagement with major cultural heritage collections
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demand forecasting or behavior modeling Computing & Data Systems Cloud computing (AWS, Azure, GCP) Big data pipelines, distributed computing, and geospatial data processing Python, R, SQL/NoSQL