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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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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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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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polymer chemistry is required. · Experience with molecular and/or surface characterization techniques (e.g., NMR, Raman, FTIR, electron microscopy, XPS) is required. · Experience with
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Investigate how dynamic stimuli manipulate catalyst electronic properties, and how these stimuli can manipulate catalytic elementary steps and reaction outcomes Perform detailed in situ / operando studies
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this work we investigate how molecular materials coupled to solid-phase scaffolds may influence molecular motion, photoinduced kinetics, charge dynamics, and assembly durability. The work will target
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involve synthesis of novel crystalline materials using molecular beam epitaxy (MBE) and characterization and modeling using a variety of transport and optical techniques, in close collaboration with a team
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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
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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