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design, advanced modeling and high-performance computing, mathematics and data analytics, AI/ML algorithm development, and accelerator operations Ability to model Argonne’s core values of impact, safety
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at a fraction of the computational cost. Recently Argonne successfully implemented, AERIS, a state-of-the-art seasonal-to-subseasonal (S2S) weather model AI model. A successful candidate will collaborate
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to quantify energy consumption, performance and cost benefits. In this role, a successful candidate will perform vehicle modelling and simulation of advanced powertrains to quantify the impacts of new component
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Overview The Argonne Wakefield Accelerator (AWA) Group in the High Energy Physics Division at Argonne National Laboratory seeks a postdoctoral research associate to conduct experimental and
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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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methodologies and tools for economic and ecological analyses of hydropower systems. The position will involve the development and use of computer models, simulations, algorithms, databases, economic models, and
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ML surrogate models for electronic structure and electrostatic potential in 2D materials Perform large-scale materials simulations (e.g., DFT, tight-binding, continuum models) to generate training and
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the AI system with beamline control systems (e.g., EPICS) to close the autonomous loop. The position requires publishing results in high-impact journals, presenting at international conferences, and
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of Cyanobacteria and carrying out experiments with those bacteria, as well as flow cytometric analysis. Key Responsibilities: Develop and optimize extraction methods for recovering ultra-long, high molecular weight
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, distributions, and dynamics in metallic, oxide, and semiconducting systems. This project integrates high-throughput and in situ TEM experimentation with AI/ML-driven image analysis and computational modeling