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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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for holotomography and to perform dynamic experiments using the Projection X-ray Microscope (PXM) instrument for studying microelectronics. As part of a collaborative team, the successful candidate will participate in
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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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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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of molecular reactions occurring at the surface of various materials. In addition, computational fluid dynamics (CFD) simulations combined with microkinetic modeling will be carried out to study the heat
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to energy conversion. The research involves exploring the excited-state structural dynamics and mechanisms of photoinduced energy and charge transfer across a wide spectrum of systems, including small
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targeted atoms-trap defects pairs to study the dynamics of near field transfer. The candidate will study how near field interactions affect the atoms’ spin property. The postdocs will work with other team
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) at Argonne National Lab (near Chicago, USA). The postdoctoral researcher will work on the development of large-scale molecular dynamics, AI and machine learning based analysis to understand ferroelectric
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at the APS, integrating x-ray optics and wave propagation models with realistic sample simulations based on dislocation dynamics and molecular dynamics of relevant materials. Significant attention needs
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. The successful candidate’s research will involve synergistic collaborations within a multidisciplinary team comprised of fellow postdoctoral appointees and staff scientists with computational fluid dynamics and