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physics, etc. Proficiency in Python or other scientific programming languages. Programming skills in numerical methods for image processing and AI/ML methods for quality improvement are advantageous
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developing machine learning surrogates and emulators for dynamical systems. Proficiency in managing large datasets and training with GPU-enabled computing resources. Expertise in numerical optimization and
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: - Comprehensive understanding of applied computational materials science, including electronic structure methods and molecular dynamics. - Experience with High-Performance Computing (HPC) systems and intelligent
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. The primary focus of this role will be in developing laboratory methods to improve recovery of microbial genomes from complex samples. Secondary focus of this role will be in maintaining cultures
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integration Optimization and stochastic modeling methodologies Energy storage Electricity market analysis Supports multidisciplinary teams in the application of these methods and tools to complex issues in
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research. The position plays a central role in strengthening the CNM user science program, with a particular focus on electron microscopy and synchrotron-based X-ray microscopy at the Advanced Photon Source
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Intelligence, Machine Learning, Quantum Information and Quantum Simulation. The successful candidate will be expected to lead an independent research program in particle theory to strengthen and complement
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programming, interfacing hardware, and developing machine-learning methods highly desirable. The researcher will join an Argonne funded project with interdisciplinary team of material scientists, computer
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The Multiphysics Computations Section at Argonne National Laboratory is seeking to hire a postdoctoral appointee for performing high-fidelity scale-resolving computational fluid dynamics (CFD
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on developing machine-learning surrogates for electronic structure and electrostatic potential and using these models to predict structural and electronic evolution under applied bias. Methods may include density