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) to work on the Terrestrial Ecosystem Science Scientific Focus Area (TES SFA). The successful candidate will contribute to the development and evaluation of the Energy Exascale Earth System Model's land
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) to work on the Terrestrial Ecosystem Science Scientific Focus Area (TES SFA). The successful candidate will contribute to the development and evaluation of the Energy Exascale Earth System Model's land
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data and develop data-driven methods for predicting land loss and ecosystem transitions in wetland-rich landscapes of the Gulf Coast with a focus on coastal Louisiana. This candidate will directly
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of large-scale geospatial and time-series datasets. The candidate will develop and evaluate multimodal AI models to characterize vegetation and land-surface dynamics and quantify ecosystem responses and
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of minerals or nanomaterials (for example Li/Na ion batteries, solid electrolytes, carbonates, REE-related structures). Preferred Qualifications: Experience in wet chemistry and/or mineralogy. Experience with
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experience in one or more separation processes such as filtration, solvent extraction, liquid-liquid extraction, solid-liquid separations, gas-liquid separation, membrane contactors, adsorption, and packed
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related field. Compiler and Systems Expertise: Strong experience with LLVM (e.g., writing passes, IR transformations) and HPC programming in C++, MPI, OpenMP, CUDA, or related models. Solid understanding
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in the areas of Hydrological and Earth System Modeling and Artificial Intelligence (AI). The successful candidate will have a strong background in computational science, data analysis, and process
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Requisition Id 17131 Overview: Oak Ridge National Laboratory is the largest US Department of Energy science and energy laboratory, conducting basic and applied research to deliver transformative
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Postdoctoral Researcher in the fields of uranium chemistry and nuclear forensics. The role entails performing and leading cutting-edge research on nuclear fuel cycle-related materials with a focus on solid