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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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researchers will work in a dynamic team of staff scientists at Argonne National Laboratory. Within the team we have extensive experience with large scale molecular dynamics simulations, first principles
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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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capability and scalability of multi-scale and multi-physics simulation codes. Develop turbulent combustion models for predictive CFD simulations of combustion dynamics in rotating detonation engines (RDEs
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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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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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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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(H2, NH3). The successful candidate will leverage high-performance computing (HPC) resources at the Laboratory to perform CFD simulations of low-carbon fuel injection, mixing, combustion, and emissions
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contributions in: Building novel generative models for predicting genome-scale evolutionary patterns using GenSLMs Developing scalable models that can, when integrated with high throughput molecular dynamics