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development of artificial intelligence (AI), machine learning (ML) and large language models (LLMs) to help promote business intelligence in the Agency (e.g., interactive AI expert systems). Integration of AI
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diseases such as Japanese encephalitis, Rift Valley fever, and related diseases. Learning Objectives: The fellow will have opportunities to learn field-based techniques related to survey and manage arthropod
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pathogens such as Japanese encephalitis and Rift Valley fever. Learning Objectives: The fellow will learn epidemiological techniques related to modeling parasitic and vector-borne diseases. Opportunities
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ecosystem services that they provide. Learning Objectives: The participant will learn to utilize ecological simulation models and to design and conduct geospatial analysis of model results to characterize
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are offered an opportunity for an independent research project using lab data to gain experience conducting ecological data analysis, manuscript writing, and publishing in peer-reviewed journals. Learning
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development. You will gain experience researching advanced in silico prediction algorithms, analyzing machine learning approaches for toxicity pattern recognition, and participating in developing standardized
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to unravel key indicators of biological relevance during seed quality testing procedures and contribute to a healthy national and international seed trade economy. Learning Objectives: Under guidance of a
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machine learning, image recognition, and prediction of damage to tree nuts from insect pests. They will also collaborate with other team members on statistical analysis of data collected as part of
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documenting progress on data processing. Opportunities may also be available to participate in field data collection at various locations in the Pacific Northwest. Learning Objectives: As an educational
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and Data Science (including machine learning and AI for defense applications) - Systems Engineering and Engineering Management - Industrial Engineering and Production Management - Mathematical Modeling