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agricultural yield prediction models for use in plant breeding through a combination of high-throughput phenotyping data, physiological crop growth modeling, and artificial intelligence methods. There will be
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virulence monitoring of domestic and international isolates. The research project entails using artificial intelligence-based structural modelling to predict plant-pathogen protein-protein interactions
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operations, senior scientists within the IC annually pinpoint vital research topics spanning diverse disciplines, from artificial intelligence, quantum computing and sensing, biotechnology, and energy and
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: Under the guidance of a mentor, the participant will learn: How to apply forms of artificial intelligence, such as machine learning, to agricultural data sets How to use generative artificial intelligence
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and weaknesses for end-users. Help develop new or improve existing soil moisture estimates using NISAR and other datasets utilizing artificial intelligence (AI) and machine learning. The outcome from
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the guidance of a mentor, this opportunity will involve: developing and applying methods in computational biology and artificial intelligence to gather information about gene function in the legume family; using
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Applied statistics Network routing Agent-based simulation Behavioral economics Game theory Decision theory Machine learning Artificial intelligence Where will I be located? Both local and remote
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-Docs, post-Bacs, summer internships, etc.) to those interested in research in the following fields: Theory and application of machine learning and artificial intelligence including Natural
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phenotyping using both drone-based and ground based sensing platforms. Learn artificial intelligence and machine learning techniques to analyze image and geospatial data from diverse sources for crop monitoring
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, biochemical, and gene expression data to determine underlying biological mechanisms Learning how artificial intelligence models can interpret biological data Documenting and writing detailed methods and results