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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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discipline • A strong experimental background in transmission electron microscopy • Experience with focused ion beam preparation of electron microscopy specimens • Experience with analysis of complex images
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magnetic thin films, patterned structures, and complex interfaces. In this advertised role, you will be conducting real-space imaging of magnetic heterostructures using LTEM to understand spin textures and
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statistics and uncertainty quantification (Bayesian analysis, data assimilation, Gaussian process modeling), and their application to complex systems modeling. Experience in developing and applying statistical
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organometallic / inorganic heterogeneous catalysis Design, synthesize, and characterize metal-ligand complexes supported on metal oxide and/or non-traditional support materials Investigate the catalytic activity
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environmental analysis, and the ability to identify topics and data sources and engage experts to conduct research. Capable of identifying key features of complex systems and processes. Well-developed problem
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
, or Julia) Experience in statistical modeling and probabilistic analysis Ability to model Argonne’s core values of impact, safety, respect, impact and teamwork Preferred skills, abilities, and knowledge
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of complex propulsion systems involving modeling of multi-phase flows, turbulent combustion, heat transfer, combustion, and thermo-mechanical fluid-structure interaction by further developing commercial/in
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The Advanced Grid Modeling group at Argonne National Laboratory's Center for Energy, Environmental, and Economic Systems Analysis (CEEESA) is seeking a highly motivated Postdoctoral Researcher
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the performance and scalability of large-scale molecular dynamics simulations (e.g. LAMMPS) using machine-learned potentials (e.g. MACE) through algorithmic improvements, code parallelization, performance analysis