33 software-defined-network-"https:" Postdoctoral positions at Oak Ridge National Laboratory
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networked systems. It develops community applications, data assets, and technologies and provides assurance to build knowledge and impact in novel, crosscut-science outcomes. The position is supported by
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experiments in the SNS ring including experiment design, and data analysis. Develop software for data acquisition and analysis as needed. Perform simulations using a well-tested model of the SNS ring to
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to ORNL's Research Code of Conduct. Our full code of conduct, and a statement by the Lab Director's office can be found here: https://www.ornl.gov/content/research-integrity Benefits at ORNL: UT Battelle
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research plan with defined goals and sound experimental design. Prioritize a safe work environment and comply with all ES&H regulations. A proven publication record and ability to produce research to a high
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at scale. Experience in HPC and associated software development for applications, middleware, and/or system software. Flexibility to adapt to diverse R&D projects and tasks. Effective communicator in both
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: Experience in one more of the following areas: Mathematical methods for kinetic and/or fluid equations Multiscale problems and model reduction Modern machine learning software tools and frameworks
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machine learning software tools and frameworks Implementation of scalable numerical algorithms on HPC architectures Excellent written and verbal communication and interpersonal skills. The ability to obtain
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-resolution microscopy, and in situ neutron or X-ray scattering and tomography methods. Strong background in computational and image-processing software, scientific programming, and high-performance computing
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on circular economy research Experience in working in the genetic algorithm and artificial neural networks is preferred. Experience in manufacturing process modeling of advanced manufacturing technologies
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descent, random forests, etc.) and deep neural network architectures (ResNet and Transformers). Preferred Qualifications: Knowledge of Approximate, Local, Rényi, Bayesian differential privacy, and other