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Knowledge of atmospheric dynamics, process scale models, and numerical computation techniques Knowledge of data analysis Knowledge of using atmospheric observational datasets, data assimilation techniques
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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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during the appointment. Diagnosing and analyzing the numerical challenges related to the narrower data width Devising and evaluating novel techniques to exploit the reduced precision hardware Incorporating
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the ability and motivation to develop expertise in large-scale model training and scaling on HPC systems, as well as in handling the unique characteristics of scientific data, including large-scale numerical
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scattering, x-ray circular dichroism, photoelectron spectra, and nonlinear x-ray signals Model ultrafast molecular dynamics using time-resolved observables, including numerical solutions of the time-dependent
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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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computational science expertise. The Computational Science (CPS) Division focuses on solving the most challenging scientific problems through advanced modeling and simulation on the most capable computers
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physics, etc. Proficiency in Python or other scientific programming languages. Programming skills in numerical methods for image processing and AI/ML methods for quality improvement are advantageous