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. The project will involve development of novel parallel algorithms to facilitate in-situ analyses at-scale for multi-million and multi-billion atom simulations. In this role, you can expect to work on enhancing
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in AI to study natural and artificial minds in parallel, creating the opportunity to make discoveries about ourselves and to find new ways to understand and improve AI systems. Appointments will be
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
mathematics, or a related field Candidates should have expertise in two or more of the following areas: Uncertainty quantification, numerical solutions of differential equations, and stochastic processes
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organic analysis as a primary means of detecting life. To optimize the science yield of such missions, it is critical to understand processes that govern maturation of organic matter prior to sampling and
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, surrogate modeling of scientific processes, workflow automation and adaptive simulation pipelines, and performance analysis and optimization. The candidate will also contribute to and help originate research
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productivity while reducing external inputs. In parallel, the lab is expanding efforts to understand microbiome-associated phenotypes that contribute to drought tolerance and soil water retention. This includes
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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
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on designing system software for automating processes such as intelligent data ingestion, preservation of data/metadata relationships, and distributed optimization of machine learning workflows. Collaborating
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range of molecular systems, including: Transition-metal complexes (e.g., chiral ruthenium and iridium complexes) Local and nonlocal inner-shell decay processes in solvated ions and transition-metal
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-scale scientific data. Publishing research in leading peer-reviewed journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources