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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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research will involve synergistic collaborations with a multidisciplinary team involving engine modelers, computational fluid dynamics (CFD) experts, and computational scientists to enhance the predictive
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efficient CFD models to simulate the fuel injection, fuel-air mixing and combustion dynamics for aerospace combustors. Develop robust libraries to accurately model non-ideal thermophysical properties of real
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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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decarbonization applications. With guidance, the appointee will: Develop advanced multiscale, multiphysics simulation tools applicable to the modeling of chemical processes and equipment relevant to chemical
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, modeling and analysis, integrating diverse data sets to identify global risks affecting sourcing strategies. In this role you will: Conduct and contribute to research and model development to enhance
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-aware multi-modal deep learning (DL) methods. At Argonne, we are developing physics-aware DL models for scientific data analysis, autonomous experiments and instrument tuning. By incorporating prior
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The Multi-Physics Computations group at Argonne National Laboratory is seeking to hire a postdoctoral appointee on the topic of CFD modeling of internal combustion engines fueled by low-carbon fuels
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years. It has developed the GREET® (Greenhouse gases, Regulated Emissions, and Energy use in Technologies) model for estimating energy use, air emissions, and water consumption on a life-cycle basis for
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., transient absorption and emission), including laser operation, optical alignment, detector interfacing, and data analysis Excellent written and verbal communication skills Ability to model Argonne’s core