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of radiance data from new hyperspectral infrared instruments such as IASI-NG, MTG-IRS Enhancement of CrIS radiance assimilation algorithm are highly encouraged. - Use machine learning methods to cope with model
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inference (otherwise known as spectral retrieval), which involves using forward models in conjuction with Bayesian or machine learning-based techniques in order to derive posteriors on parameters of interest
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uses cryo-electron microscopy to understand how viral proteins are recognized by antibodies at an atomic level, and how that recognition can be exploited to design effective vaccines. You will learn
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accurate image labeling and annotation to support supervised machine learning applications. Prepare and gain experience through field experiments, including protocol development, equipment setup, and data
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high school seniors, who are pursuing undergraduate studies in STEM, with an opportunity to explore the world of agricultural science through hands-on learning experiences. Participants will have the
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clinical research with an emphasis on novel technology development. Why should I apply? Under the guidance of a mentor, you will learn and gain hands-on experience to complement your education and support
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their skills in XR development. Why should I apply? Under the guidance of a mentor, you will learn and gain experience in a variety of research activities, including: Designing, developing and applying XR
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necessary to become credentialed as a Principal Investigator Applying a broad range of statistical and machine learning methods to human performance data collected in real-world settings Developing
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shifts and geochemical fluxes within the vadose and groundwater zones. Learning Objectives: This summer program provides an opportunity to gain hands-on experience with Forest Service monitoring protocols
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of boreholes, and utilizing instrumentation to observe how fire impacts water movement through the vadose zone. Learning Objectives: This summer program provides an opportunity to gain hands-on experience with