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areas. These include, but are not limited to: Applying machine learning algorithms to solve real-world problems. Creating and structuring databases for storage, retrieval, and image analysis. Determining
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promoters. Digital Phenotyping: Application of hyperspectral imaging and advanced imaging tools to detect disease traits beyond the visible spectrum. AI-Driven Data Analysis: Leveraging machine learning
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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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regions will be evaluated for features such as signatures of selection or diversifying or purifying selection, around genes and regions of agricultural importance. Learning Objectives: The participant will
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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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Learning about protein design and engineering Exploring cell-based and cell-free screening Applying high-throughput screening Utilizing bioinformatics, machine learning, and other computational approaches
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? As the selected participant, you will primarily engage in research activities with the Senior Scientist for the CFI Military Performance Laboratory; a laboratory which conducts translational research
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and stress), behavior (grazing behavior halters, accelerometer-based ear tags, chute velocity measures), environmental monitoring, and pasture measures. Learning Objectives: Under the mentor's guidance
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the underlying impacts of stressors on bee health and analytical skills for understanding colony level dynamics and predicting mass colony loss events. Learning Objectives: The fellow will learn and apply
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Raman imaging technologies for safety and quality evaluation of agricultural products. Learn artificial intelligence/machine learning methods to evaluate hyperspectral image data to assess safety and