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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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The Applied Materials Division (AMD) in the Emergent Materials and Process Group at Argonne National Laboratory in looking for a Post-doctoral Appointee -- Pyrometallurgy. The candidate will perform
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including engineering, economics, and environmental science. Experience developing mathematical or computational models for simulation and optimization of energy/economic systems in ASPEN Plus® and/or Julia
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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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of molecular reactions occurring at the surface of various materials. In addition, computational fluid dynamics (CFD) simulations combined with microkinetic modeling will be carried out to study the heat
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processes in internal combustion engines (ICEs), such as fuel injection, combustion, heat transfer, etc. Improve, develop, and implement CFD sub-models necessary to enable predictive ICE simulations
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at the APS, integrating x-ray optics and wave propagation models with realistic sample simulations based on dislocation dynamics and molecular dynamics of relevant materials. Significant attention needs
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candidate will work on cutting-edge research integrating genome-scale language models (GenSLMs) with deep mutational scanning data, and experimental virology to predict viral evolution and identify emerging
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microscope, as well as electrostatic beam blanker or ultrafast pulser in electron microscopes. Proficient in data analysis and modeling, with experience using Python and other programming or simulation tools
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real-time simulation platforms. Knowledge with designing, modeling, or hardware prototyping of power electronic converters. Proficiency in Python and/or C. Ability to work both independently and