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turbulent combustion applications, as well as parallel scientific computing. Knowledge of deep machine learning (using TensorFlow, PyTorch, etc.) for multi-fidelity modeling, regression tasks, management and
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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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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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Electrical Engineering, Computer Science, Operations Research, or a closely related field. Experience in power systems, distribution systems, or microgrid modeling, with a solid understanding of advanced co
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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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scholarly work or industry experience in economic and supply chain analysis, computational modeling, or policy analysis. Excellent oral and written communication skills in scientific and engineering contexts
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physics phenomenology. Members in the Particle Theory Group work on physics within and beyond the Standard Model, phenomenology at current and future colliders, as well as emerging areas in Artificial
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The Nuclear Science and Engineering (NSE) Division is seeking a postdoctoral appointee to develop computational methods and computer codes to model the physics and engineering of advanced nuclear
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We are seeking a highly motivated Postdoctoral Appointee with a strong background AI/ML specifically in the development and application of Large Language Models (LLMs) tailored for scientific use
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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