72 model-driven-engineering-"Data-driven-Materials-Modeling" Postdoctoral positions at Oak Ridge National Laboratory
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Requisition Id 17091 Overview: The Environmental Sciences Division at Oak Ridge National Laboratory (ORNL) is seeking a Postdoctoral Research Associate to join the Earth Systems Modeling Group (ESMG
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Requisition Id 17180 Overview: We are seeking a Computational Physicist to carry out modeling of plasma transport and plasma-material interactions (PMI) in the Materials Plasma Exposure eXperiment
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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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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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Requisition Id 17092 Overview: The Environmental Sciences Division at Oak Ridge National Laboratory (ORNL) is seeking a Postdoctoral Research Associate to join the Earth Systems Modeling Group (ESMG
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Requisition Id 17142 Overview: We are seeking a Postdoctoral Research Associate who will focus on urban-scale energy modeling. This position resides in the Grid Interactive Control Group in
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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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Application-driven Composable Distributed Storage. The candidate will be able to make research contributions in understanding and efficient use of distributed data storage and I/O subsystems for High
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Engineer will conduct R&D in nuclear nonproliferation with expertise in computational nuclear reactor physics through modeling and simulation (M&S). The candidate will perform analysis and methods
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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