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and methodologies. Experience analyzing and interpreting spatial datasets using GIS software (e.g., ArcGIS). Experience conducting meta-analyses or applying meta-analytical statistical techniques
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manuscripts for peer-reviewed publications Analyze complex datasets across spatial and temporal scales using advanced statistical and data science approaches Present research findings in group meetings
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agronomic datasets from cover crop experiments using advanced statistical and quantitative genetic approaches. Apply genomic prediction methods to evaluate and improve cover crop performance. Design
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, transcriptomic, and proteomic datasets to uncover biological mechanisms and generate novel research insights. The successful candidate will apply bioinformatics and statistical approaches to analyze high
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. The successful candidate will also apply statistical modeling for mechanistic inference and conduct biogeographic and geospatial analyses of microbiomes. This position involves collaborating with scientists and
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post-acquisition bioinformatic and/or statistical analysis. Experience analyzing high-resolution LC-MS/MS data generated using Thermo Scientific Orbitrap instrumentation or similar high-resolution mass
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, design, plan, and execute wet-lab experiments, conduct bioinformatic and statistical analyses, and write peer-reviewed journal articles. Key Responsibilities Design and implement experiments that pair
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pangenomics and population diversity, with opportunities to contribute to similar analyses in other crop systems, including cotton. Required Minimum Qualifications: PhD in Plant Biology Experience conducting
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at appropriate meetings, conferences, and seminars. Required Minimum Qualifications PhD in Neuroscience or equivalent degree Preferred Qualifications: One year of human subjects research experience One year of
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experience PhD with an emphasis in animal welfare, awarded by the position start date Must have an IA license to practice veterinary, USDA category II accreditation & ability to acquire controlled drug