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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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. The successful candidate should have expertise and experience in process modeling, techno-economic analysis (TEA) and life cycle analysis (LCA) of lithium-ion batteries and/or recycling and resources to products
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The Theory and Modeling Group at the Center for Nanoscale Materials (CNM), Argonne National Laboratory (near Chicago, Illinois), invites applications for a postdoctoral appointment focused on theory
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Argonne National Laboratory seeks a postdoctoral researcher to help build a high-resolution coastal-urban flooding modeling capability within the Energy Exascale Earth System Model (E3SM
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-principles and atomistic simulations with machine-learned interatomic potentials to: Model reaction pathways on metal-oxide surface, including adsorption, reactions and diffusion steps. Construct atomistic
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and novel device technologies Develop, validate, and maintain simulation and modeling frameworks for detector performance, characterization, and benchmarking Analyze simulation results and experimental
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processes and develop models of material interactions and behavior in molten salt environments. Develop novel and improved methods for measurements of molten salt properties and standardizing procedures
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agencies and other national laboratories. The candidate will develop power systems and electricity market modeling, and analytics tools that support energy, economic, and financial analyses of power grid
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and teamwork capabilities, with a track record (or potential) in writing publications and presenting scientific results Ability to model Argonne’s core values of impact, safety, respect, integrity, and
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foundational models to describe IDP interactions under various physiological conditions, both normal and cancer related Use these models to iteratively design, validate, and refine experiments, leading