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dynamics. This position focuses on advancing fundamental understanding of light-matter interactions with direct relevance to energy conversion. The research involves exploring the excited-state dynamics and
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the performance and scalability of large-scale molecular dynamics simulations (e.g. LAMMPS) using machine-learned potentials (e.g. MACE) through algorithmic improvements, code parallelization, performance analysis
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microscopy of materials and nanostructures for electronics. This capability at Argonne’s Center for Nanoscale Materials enables imaging of electrically driven dynamics with simultaneous nanometer-scale spatial
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The Data Science and Learning Division (DSL) at Argonne National Laboratory is seeking a postdoctoral researcher to conduct cutting edge molecular and microbiology work to enhance non-proliferation
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
modeling of large-scale dynamics in networks. This role involves creating large scale models of dynamic phenomena in electrical power networks and quantifying the risk of rare events to mitigate
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specifically on developing machine learning-based surrogates and emulators for the dynamics of power grids. This role involves creating advanced probabilistic models that capture the complex behaviors
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for dynamics imaging. The primary goal of this project is to develop single-frame ptychography methods that eliminate the need for scanning, enabling fast imaging and the visualization of dynamic processes in
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will be part of a dynamic team working collaboratively with researchers in Q-NEXT (both at Argonne and other academic and industrial member institutions), and is expected to build on and create new
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modeling of x-ray spectroscopies sensitive to molecular chirality; simulations of x-ray–induced ultrafast electron-transfer, decay, and nuclear dynamics in gas- and liquid-phase systems; and the development
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and heterointerfaces. The postdoc will lead experimental design, data acquisition, and quantitative reconstruction. The appointees will work within a highly collaborative team spanning multiple DOE user