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). This position will focus on ultrafast dynamics in femto- to nanosecond time-domains in quantum materials including nonequilibrium phase transitions and collective excitations in quantum materials, including
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candidate would be a PhD in geophysical sciences, computer science, or machine learning with experience in developing and verifying deep learning-based models for large dynamical systems (e.g. weather
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all areas of experimental condensed matter physics will be considered, particular emphasis will be placed on the dynamical studies of 2D materials and their functionalities. This postdoctoral position
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. Working within an interdisciplinary team, you will develop frameworks that connect atomistic features, mesoscale dynamics, and device-level performance. The effort will integrate heterogeneous data from
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computational research in accelerator science and technology. The focus is on developing and applying machine learning (ML) methods for accelerator operations and beam-dynamics optimization in advanced
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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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would be part of a dynamic team working collaboratively with researchers in Q-NEXT (both at Argonne and other member institutions) on both hardware development and network-scale benchmarking of memory
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phenomena Create new reduced-order models and submodels related to fluid flow, heat transfer, thermochemistry, and electrochemistry in reactive systems Use modeling tools such as computational fluid dynamics
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staff scientists, engineers, postdocs, and students, with an active research portfolio that spans: Leading electron-scattering experiments at Jefferson Lab (CLAS12, SoLID, Hall A, B, and C) Major
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total X-ray scattering (TXS) and pair distribution function (PDF) analysis capabilities and methodology to study laser-driven structural dynamics in functional materials. This position is part of a