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Field
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Associate in PFAS Predictive Modeling with the University of Maine Cooperative Extension will establish and sustain a dynamic research and educational outreach program to address data and modeling needs
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superconducting behavior, utilizing NV centers in diamond and spin defects in 2D materials in variable-magnetic-field cryogenic environments. The candidate will leverage single- and ensemble-spin based sensors
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with Dr. Emily Becker and the NOAA Climate Prediction Center. Key responsibilities Train neural networks and quantify uncertainty to evaluate predictability Perform explainable AI (XAI) related research
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disparities in supportive care medication (SCM) use, using national datasets (e.g., SEER-Medicare, SEER-MHOS), predictive analytics, and stakeholder-engaged methods. The lab is committed to actionable research
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candidate will work on cutting-edge research integrating genome-scale language models (GenSLMs) with deep mutational scanning data, and experimental virology to predict viral evolution and identify emerging
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Vitro to In Vivo Extrapolation of Toxicant Effects on Ovarian Function” and will focus on phthalates and developing models that extrapolate in vitro assay results to predict in vivo effects on ovarian
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environmental science is of supreme importance. Students entering the science classroom bring well-developed intuitive frameworks that help them understand, explain, and predict the world around them. These
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contribute. Since early risk prediction and timely intervention can mitigate vision loss, it is critical that we fully understand the underlying cause(s). This project aims to identify biomarkers in the eye
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 12 hours ago
predictive modeling and pathfinding. This includes exploring the use of large language models (LLMs) for schema mapping and normalization tasks, evaluating embedding strategies that enhance interpretability
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strategy will be used to design an optimum test matrix. This framework will enable modeling and predicting ion-irradiated mechanical properties using reduced test data, thereby accelerating the development