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requires not only expertise in LLMs and machine learning but also an understanding of the unique challenges posed by scientific data, which often includes large-scale numerical datasets, complex simulations
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calculations, reactive empirical force fields, chemical dynamics, deep learning and numerical algorithms, data analysis, experimental characterization and imaging. Our research has involved methodology and
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
mathematics, or a related field Candidates should have expertise in two or more of the following areas: Uncertainty quantification, numerical solutions of differential equations, and stochastic processes
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developing machine learning surrogates and emulators for dynamical systems. Proficiency in managing large datasets and training with GPU-enabled computing resources. Expertise in numerical optimization and
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such as PyTorch and TensorFlow. Experience with high-performance computing and/or scientific workflow. Strong background in inverse problems, numerical optimization and image processing. Job Family
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of reactor systems. Knowledge of heat transfer and fluid dynamics for nuclear system applications and reactor safety analyses. Knowledge of computation methods and numerical solvers for engineering
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or soon-to-be-completed PhD (typically completed within the last 0-5 years) in Electrical Engineering Fundamental understanding of power system modeling, numerical methods and linear control theory