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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
modeling of large-scale dynamics in networks. This role involves creating large scale models of dynamic phenomena in electrical power networks and quantifying the risk of rare events to mitigate
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candidate will participate in RNA project for structural characterization of RNA by SAXS, x-ray crystallography, and Cryo-EM. The main role for the project is to produce RNA samples in large scale by using in
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running simulations or AI workflows on supercomputers Experience with training or applying large language models for research Experience with MPI and Input/Output (I/O), and data management Experience in
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advanced computing, optimization, and data analytics technologies. The postdoctoral researcher will work with a team of researchers on solving challenging problems using optimization, stochastic models
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design, develop, and evaluate AI-driven scientific visualization assistants that support intuitive, context-aware interaction with large-scale simulation and experimental data. The postdoc will focus
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simulation, TEA and LCA, and have a good knowledge of current and future resource recovery and separation technologies. The successful candidate will 1) collect data pertaining to battery recycling, battery
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for candidates interested in the intersection of complex oxide epitaxy, quantum information, and nanophotonic to contribute to high-impact science at a national user facility. Key Responsibilities Develop and
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, and contributing to a robust data infrastructure that makes large-scale, multimodal datasets FAIR and AI-ready. You will publish findings in high-impact journals, present at major international
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the ability and motivation to develop expertise in large-scale model training and scaling on HPC systems, as well as in handling the unique characteristics of scientific data, including large-scale numerical
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). Expertise in data and model parallelisms for distributed training on large GPU-based machines is essential. Candidates with experience using diffusion-based or other generative AI methods as