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Organization U.S. Department of Agriculture (USDA) Reference Code USDA-ARS-SEA-2026-0267 How to Apply To submit your application, scroll to the bottom of this opportunity and click APPLY. A complete application consists of: An application Transcript(s) – For this opportunity, an unofficial...
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inference) Algorithmic development for bilevel (or multilevel) optimization Methodological developments in Bayesian statistics and/or decision analysis Application of adversarial risk analysis within security
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, molecular biologists, bioinformaticians, epidemiologists, and other public health professionals. Research and training activities may include: Participating in the development, optimization, evaluation, and
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; preparing biological samples for testing; coordinating and optimizing insect behavioral assays Data Management: maintaining meticulous experimental data records, digital database entry, and basic statistical
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industrial desert crop to farmers in the south western US as an alternative to high water requiring crops such as as cotton and alfalfa. Current methods will be optimized and new methods may be invented and
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process of continuous germplasm improvement, discovery trait research and methodology optimization to reach greater breeding efficiency. The general research technologies/methodologies and approaches
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-resistance phenotypes. You will gain experience applying AI, genomics, and bioinformatics, from foundational AI/ML concepts to hands-on application and optimization of DNA and protein language models
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data collected longitudinally across the post-infection study time course to optimize machine learning methods that predict disease outcomes and identify host factors and interactions that most heavily
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of microbial genetics and physiology to optimize fermentation strategies through novel strain development and modification of bioprocessing parameters. Learning Objectives: You will gain experience in
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techniques. You will have the opportunity to participate in various projects utilizing artificial intelligence (AI) and machine learning (ML) to develop applications that optimize combat casualty care