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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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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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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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and Data Science (including machine learning and AI for defense applications) - Systems Engineering and Engineering Management - Industrial Engineering and Production Management - Mathematical Modeling
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plants. The participant will learn and use multiple molecular biology, synthetic biology and plant biotechnology related tools and techniques including plasmid vector design and assembly, plant genetic
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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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-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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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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, 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