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The Center for Nanoscale Materials (CNM) at Argonne National Laboratory is seeking postdoctoral researchers to work on distributed quantum computing. The project aims to develop superconducting
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. This position offers an exciting opportunity to work at the intersection of HPC and AI, addressing critical communication bottlenecks and optimizing network interconnects for large-scale distributed systems
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for projects related to the integration of microgrids, distributed energy resources (DERs), and advanced transmission & distribution (T&D) coordination. This role is ideal for a researcher passionate about
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, AC and DC power sources, power electronic converter modules, battery energy storage system, and AC/DC loads. Develop MATLAB/Simulink models for distribution and transmission systems that include
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; mathematical and algorithmic underpinnings of machine intelligence; explainable artificial intelligence (XAI); physics-aware artificial intelligence (PAI); and algorithms and techniques for materials discovery
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calculations, reactive empirical force fields, chemical dynamics, deep learning and numerical algorithms, data analysis, experimental characterization and imaging. Our research has involved methodology and
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reconstruction algorithms that incorporate multiply-beam coherent scattering imaging in a grazing incidence geometry to improve the spatial resolution to ultimately demonstrate the utility of the novel coherent
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be to develop high fidelity simulations and/or algorithms to enable Bragg coherent diDraction imaging. We expect x-ray ptychography and coded aperture methods to play a fundamental role in creating a
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. The project will involve development of novel parallel algorithms to facilitate in-situ analyses at-scale for multi-million and multi-billion atom simulations. In this role, you can expect to work on enhancing
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
Knowledge in modeling and algorithms for large-scale ordinary differential equations (ODEs) and differential-algebraic equations (DAEs) Proficiency in a scientific programming language (e.g., C, C++, Fortran