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long autoregressive time series, diagnostics and model comparison, sensitivity and identifiability analysis, intuitive model interrogation methods and calibration when observations are few or
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), and data-model integration, leveraging the U.S. Department of Energy’s (DOE) Leadership-Class Computing Facilities to advance predictive understanding of complex environmental systems. Major Duties
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ecosystem processes. The position offers the opportunity to work at the interface of model development, observational data synthesis, and emerging AI/ML methods, in close collaboration with researchers from
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advance the design and operation of next-generation thermal systems, developing strategies to model, optimize, predict, and autonomously control thermal systems behavior in complex systems. The candidate
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fungal systems and genetics. The successful candidate will be part of the Synthetic Biology section ( https://www.ornl.gov/group/synthetic-biology ) and will contribute to a Laboratory Directed Research
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reference methods, and model training. Implement and optimize large-scale MD simulations using MLIPs and other atomistic models on HPC resources. Evaluate, integrate, and optimize different MLIP
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environments. The candidate will be expected to model prototypes of the fiber optic based sensors using standard Multiphysics software, perform out-of pile laboratory testing of the instruments in
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transport. This will involve designing efficient and trustworthy ML algorithms for physical systems, scalable data loading and training methods for exascale computing platforms, and model verification and
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calibration approaches and apply machine learning and Bayesian calibration methods to enable multi-scale, multi-physics model development. Complete simulation verification, model validation
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Model, working closely with the experimental team to understand this device’s PMI physics and optimize performance of the device in its upcoming campaigns. The position resides in the Power Exhaust and