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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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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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. Knowledge of C/C++ language and parallel programming with MPI. Background in modeling of engineering systems. Familiarity with high performance computing (HPC) software platforms is a plus. Skills in using
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and energy conversion systems. Knowledge of computational techniques and numerical methods. Knowledge of computer simulation and data analysis. Knowledge of C/C++ language and parallel programming with
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relevant numerical methods to dramatically reduce time to a feasible solution, parallelization of computations/high-performance computing, and other emerging and novel techniques to improve the efficiency
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is preferred. Research experience in one or more of the following areas: 1.) Complex systems modeling, including simulation or analytical modeling, 2.) High performance computing, parallel programming
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(2DIR), and 2D electronic-vibrational (2DEV) spectroscopy are desirable but not necessary Familiarity with experimental setup, including computer interfacing and electronics Job Family Postdoctoral Job
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The Mathematics and Computer Science Division (MCS) at Argonne National Laboratory is seeking a Postdoctoral Appointee to conduct cutting-edge research in scientific machine learning, focusing
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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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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