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Island. This learning opportunity will include synthesizing information on various mitigation methods (e.g., shaded fuel breaks, fuel reduction treatments, post-fire restoration), developing cost scenarios
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) participant, you will join a community of scientists and researchers in an effort to optimize collection methods and protocols for human biofluids. This project will be in support of the Air and Space
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the production, value, and safety of pecan, peach, nectarine, and plum crops. The postdoctoral fellow will help in implementing experiments to develop novel methods of controlling economically important pests
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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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businesses to develop new technologies that enhance the nutritional and functional qualities of plant-derived foods and to develop novel methods to detect contaminants and contents in foods that affect food
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to integrate diverse perspectives and convergent methods in addressing complex social dimensions of wildland fire management. Mentor: The mentor for this opportunity is David Flores (david.flores2@usda.gov
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• Experience with protein purification • Record of publications in peer-reviewed journals • Knowledge of immunology, virology • Experience with structural biology software (Pymol, Phenix, Chimera, etc) Point
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breeding and molecular biology. Learning Objectives: As a result of this experience, the participant will: Learn methods to conduct transformation of sugarcane, Learn genomics, bioinformatic methods
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, pharmacovigilance, pharmacoepidemiology methods development). In general you will have opportunities to learn: Understanding of pharmacovigilance workflows; GenAI based algorithm and agent development for causality
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, or unsupervised learning methods. Proficiency in Python and familiarity with scientific computing libraries such as PyTorch, TensorFlow, Pandas, NumPy, and related ML frameworks. Experience with large datasets