13 model-driven Postdoctoral positions at Oak Ridge National Laboratory in United States
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Experience with deep learning frameworks such as PyTorch or TensorFlow Exposure to AI-enabled scientific workflows that couple simulation with data-driven modeling, including emerging approaches involving
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through data-driven modeling and optimization. The successful candidate will work at the intersection of thermal-fluid sciences, control theory, and artificial intelligence/machine learning to advance
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Landscape analysis and modeling, with a focus on training and application of predictive machine learning tools. This researcher will synthesize regional-scale remote sensing imagery and ecological monitoring
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quantum simulations of materials or chemical physics methods, in particular ab initio and AI-driven simulation methods Experience in statistical mechanical theory to analyze the simulation results Basic
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-institutional collaboration focused on disturbance-driven ecosystem transitions and their impacts across the United States Gulf Coast. EGRET employs an integrated model–experiment (ModEx) approach accelerated by
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) project, an interdisciplinary and multi-institutional collaboration focused on disturbance-driven ecosystem transitions and their impacts across the U.S. Gulf Coast. EGRET employs an integrated model
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in the areas of Hydrological and Earth System Modeling and Artificial Intelligence (AI). The successful candidate will have a strong background in computational science, data analysis, and process
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-generation, data-driven manufacturing systems that integrate artificial intelligence, real-time sensing, and digital twins to transform how critical components are designed, produced, and qualified
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Computing (HPC) system architecture and intelligent storage design. The candidate will contribute to research and development efforts in scalable storage and memory architectures, telemetry-driven system
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modeling, optimal power flow (OPF), surrogate modeling, and data-driven analysis of large-scale electric power system simulations on DOE leadership-class computing resources. The candidate is expected