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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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operando experiments under electrical, thermal, or mechanical bias to capture real-time defect dynamics. Integrate multimodal datasets and collaborate with AI/ML teams for data fusion, physics-informed model
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://arxiv.org/abs/2509.00098 ) This project sits at the intersection of artificial intelligence and materials characterization and modeling. The goal is to create an AI system that can intelligently operate
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specifically on developing machine learning-based surrogates and emulators for the dynamics of power grids. This role involves creating advanced probabilistic models that capture the complex behaviors
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experience with the application of multiphysics modeling to model complex physical phenomena. Strong interpersonal, written, and oral communication skills. Ability to model Argonne’s Core Values: Impact
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) simulations and reduced order modeling of turbulent and reacting flows relevant to advanced propulsion and power generation systems, such as gas turbines and detonation engines. The successful candidate’s
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
: Expertise in rare event simulation, deep learning, and developing computationally efficient approaches for simulation and modeling in complex systems is highly desirable Experience with parallel computing
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integration Optimization and stochastic modeling methodologies Energy storage Electricity market analysis Supports multidisciplinary teams in the application of these methods and tools to complex issues in
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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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simulations and experiments across scientific user facilities, leveraging data to understand complex material phenomena across scales. Key Responsibilities Design, implement, and validate physics-informed AI/ML