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and research in several areas. These include, but are not limited to: Adversarial location and network interdiction models Adversarial machine learning attacks and defense (e.g., against Bayesian
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Implementing Bayesian networks and uncertainty quantification techniques to account for sensor noise and model confidence limits Designing, training, and fine-tuning computer vision models to extract clinically
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identifying, modifying and developing valuable functionality in cotton that will target markets where the value of domestic cotton products is assessable and shows greatest promise for high value and volume
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mortalities in United States swine herds. Efforts may result in identifying novel biological targets for more effective PRRSV prevention and treatment strategies that modulate early host immune responses
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for supplies and travel essential for the fellow's research. Research Project: Under the guidance of a mentor, you will conduct research on non-targeted analysis of per- and polyfluoroalkyl substances (PFAS) in
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evaluate new reagents, technologies, and laboratory workflows for comprehensive viral detection. Developing and evaluating molecular approaches including PCR-based sequencing, targeted NGS, pan-viral
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ecology, remote sensing, and computer science. UAV imagery and increasingly sophisticated and targeted AI algorithms can estimate forage quality, biodiversity, and the abundance of individual plant species
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evaluate protein variation and predict maize–fungal protein interactions, connecting host and pathogen variation with disease outcomes and targeted disease- and mycotoxin-control strategies. Learning
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, transgenic/gene-targeted mouse models, and natural hosts (e.g., cattle, deer, and sheep). Note: Research will be carried out in BSL-2+ or BSL-3 laboratories. The fellowship activities involve utilizing
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, you will gain significant knowledge and experience related to the modeling and analysis of weapons systems interactions with targets and the response and damage of targets to weapons effects. Where will