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for Physical Sciences (LPS) at the University of Maryland, College Park. The LQC pursues disruptive qubit research, innovative workforce development programs, and deep, collaborative partnerships to tackle some
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understanding of coastal wetland responses to sea level rise as part of a human managed system and learn about developing tools to transfer research to actionable science for coastal wetland managers
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learning models to enhance predictive capabilities related to disease outbreaks. You will be immersed in a dynamic research environment, contributing directly to the fight against vector-borne diseases
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growth. Learning Objectives: Learn to isolate bacteria and fungi of interest from the swine GI tract Learn to co-culture isolated microbes to understand microbe-microbe interactions in the swine gut Learn
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are not limited to: Learning pre-analytical, analytical, and post-analytical aspects of interpreting complex human biological material for specialized molecular genetic analysis. Gaining experience in all
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results, and collaborations. The Fellow will also have opportunity to engage with project collaborators to develop and enhance their network of subject matter experts. Outreach may include travel. Learning
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. Qualifications The ideal candidate should have a strong background in the mathematical and computational aspects of modeling subsurface and surface flows. Knowledge in machine learning, data assimilation, and
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climate adaptation. Learning Objectives: Understand and facilitate interactions at the intersection between science and land management. Learn about silvicultural and natural resource management issues in
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of this project is to learn about data collection for HAB monitoring, and to help support forecasting data collection to mitigate the impacts of such blooms. This will involve collaborating with a variety of NOAA
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-on experience to support your academic and professional goals. The participant will have the opportunity to engage in research and learning activities focusing on several important unknowns, including: Can we