80 model-driven-development "Integreat Norwegian Centre for Knowledge driven Machine Learning" Postdoctoral positions at Oak Ridge National Laboratory
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characteristics and system-level requirements, and their suitability for scientific applications. The candidate will develop modeling and simulation capabilities to quantitatively evaluate quantum, classical, and
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databases to assess ecosystem response to environmental forcing. The primary research focus is AI-forward, experiment-driven. This position encourages leveraging AI/ML methods to assess plant physiological
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(NSSD) at Oak Ridge National Laboratory (ORNL). The NND performs research and development to detect, characterize, and monitor nuclear fuel cycle activities and nuclear explosions; develops concepts
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credible claims of quantum advantage. Develop and apply physics-informed AI/ML and digital-twin capabilities to improve modeling, parameter inference, uncertainty assessment, and adaptive feedback between
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-preservation algorithms. The successful candidate will develop cutting-edge differential privacy techniques for large-scale models across multiple institutions. This position offers a unique opportunity to work
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such as quantum and analog computational models. You will explore how compilers, runtimes, and AI-driven agents can co-optimize complex architectures, reasoning across conventional processors (CPUs/GPUs
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of irradiated ceramics and alloys for tritium technology development using advanced experimental and computational methods. The researcher will perform characterization of model systems using techniques such as
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related physical sciences for a broad range of energy, transportation, and advanced manufacturing applications. The MsM group also develops artificial intelligence (AI) and foundational ML models
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this role, the postdoctoral researcher will focus on developing foundation models for fluid and plasma dynamics, including aerodynamics, turbulence, and edge/scrape-off-layer transport. This will involve
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and porting of scalable science applications and algorithms for OLCF and other leadership computers. Debugging and tuning applications for high performance. Developing performance models of existing