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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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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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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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://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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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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. The primary focus of this role will be in developing laboratory methods to improve recovery of microbial genomes from complex samples. Secondary focus of this role will be in maintaining cultures