28 optimization-nonlinear-functions Postdoctoral positions at Oak Ridge National Laboratory
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alloys and extreme environment structural materials. A strong background in mechanical behavior of materials is required. Demonstrated experience in the implementation of nonlinear constitutive models in
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alloys and extreme environment structural materials. A strong background in mechanical behavior of materials is required. Demonstrated experience in the implementation of nonlinear constitutive models in
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systems 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
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toolchain to support autonomous “closed-loop” optimization environments, enabling AI agents to analyze, transform, and validate IR with performance-driven reasoning. HPC System Co‑Design : Investigate
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toolchain to support autonomous “closed-loop” optimization environments, enabling AI agents to analyze, transform, and validate IR with performance-driven reasoning. HPC System Co‑Design: Investigate
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opportunity to work with the world's first exascale system, the Frontier supercomputer, and collaborate with experts in machine learning, optimization, electric grid analytics, and image science. The
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relationships, and distributed optimization of machine learning workflows. Collaborating with world-class scientists, you will enhance your expertise in resource optimization, scalable computing techniques
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, LabVIEW, or comparable scientific software. Preferred Qualifications: Experience with squeezed light generation and characterization, nonlinear optics, optical phase locking, and low-noise
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systems. The research will integrate multimodal transportation data, digital twins, vehicle dynamics and energy models, AI-based control and optimization, connected infrastructure, vehicle-in-the-loop
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candidate will support research and development projects that advance the state of the art in machining science, machine tool design and characterization, manufacturing process optimization, and