48 parallel-computing-numerical-methods Postdoctoral positions at Argonne in United States
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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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that integrate simulation, machine learning, and data analysis. Numerical optimization methods (e.g. machine learning including deep neural networks, reinforcement learning, data mining, genetic algorithms
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development. Knowledge of parallel scientific computing. Ability to meet project needs and tight deadlines. Present and publish results in peer reviewed papers and/or journal articles. Skilled verbal and
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multidisciplinary team, the Postdoctoral Appointee will work at the intersection of AI/ML, climate science, and high-performance computing. The candidate will develop LLMs specifically designed to understand, process
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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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by multiple orders-of-magnitude. This is an exciting opportunity to be at the forefront of using advanced computational methods and systems, including machine learning, to develop data and computing
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and methods, fostering innovation and accelerating progress in the development of efficient solar energy conversion technologies. Position Requirements Recent or soon-to-be-completed PhD (within
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The X-ray Imaging Group (IMG) of the Advanced Photon Source (APS) is seeking a postdoctoral researcher with expertise in computational science and image processing to develop innovative methods
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., Argonne’s TDCosim) for integrated analysis of microgrids, DERs, and utility-scale systems; implement methods to synchronize transmission-focused simulators with distribution-level tools. Demonstrate
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The Advanced Photon Source (APS) at Argonne National Laboratory invites applications for a postdoctoral position focused on developing novel computational approaches for multi-modal biomedical image