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and targeted, computationally expensive, model simulations. This experimental design process is envisioned to update iteratively as new data become available to optimally infer surface fluxes across
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in chemistry and biology, approaches for extracting relevant information from foundation models, and/or methods for adaptive experimental design such as active learning or Bayesian optimization
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reporting procedures for any incident or event that did affect or potentially could affect the project goals and workflow. Optimize protocols and improve methods currently employed. Coordinates work
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that are relevant to industry demands while working on research projects in SIT. The researcher will be part of the team of the MCCS NATURE Project (https://www.nparks.gov.sg/Cuge/Programmes-Schemes/Research
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
learning, and create personal and professional sustainability. We optimize our partnership with the UNC Health System through close collaboration and commitment to service. OUR VISION Our vision is to be