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Researcher will develop machine learning (ML)-based methods for reaction dynamics simulations in both gas-phase and condensed-phase systems. The primary focus of this position is the development and
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, and GC-MS platforms. The individual will collaborate with faculty, staff, students, and external researchers to design experiments, prepare samples, acquire and analyze data, and interpret complex
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microbial ecology with cultivation approaches. Additional work will include imaging-based physiology, molecular and multi-omic workflows, and quantitative and computational analyses of microbial communities
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computer vision models and web-based decision support tools, to enhance efficiency and decision-making in swine production. Required Minimum Qualifications: • Ph.D. in Animal Science, Swine Nutrition
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, interdisciplinary research environment. Key Responsibilities Develop and advance non-thermal plasma-based technologies for: Nitrogen fixation Valorization of renewable feedstocks Conversion of waste streams
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, interdisciplinary research environment. Key Responsibilities Develop and advance non-thermal plasma-based technologies for: Nitrogen fixation Valorization of renewable feedstocks Conversion of waste streams
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population genomic analyses of crop species Experience managing and analyzing large-scale biological datasets Experience using SLURM-based high-performance computing systems Demonstrated proficiency in R
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. Preferred Qualifications: Experience working with single-cell ATAC-seq, single-cell RNA-seq, and/or other emerging next-generation sequencing technologies. Experience preparing droplet-based single-cell and
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, modifying, and functionalizing porous materials (especially zeolites, oxide, and carbon-based materials) and single-site catalysts. Demonstrated extensive experience characterizing catalytic materials
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experimental and on-farm datasets from corn and soybean production systems. Process and interpret drone-based and remote sensing imagery. Conduct APSIM simulation modeling and related analyses. Investigate