53 software-defined-network-phd Postdoctoral positions at Oak Ridge National Laboratory
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computational mesh generation. In this role, you will apply your software engineering skills to develop and validate computational results that support large-scale, physics-based simulations across a variety of
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contributors to engineer solutions for developers, and users of nuclear energy modeling and simulation software. Major Duties/Responsibilities: Develop and implement numerical algorithms in advanced modeling and
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Confidential Computing and Secure Multi-tenancy. The candidate will be able to make research contributions in areas of system software architectures to support secure computing enclaves on large scale HPC and
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computing. Redesign of storage systems to meet evolving demands in AI/ML and edge-to-HPC workflows, including support for data movement, retention policies, and user-defined storage behaviors. Major Duties
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scientific outputs that may include peer-reviewed publications in top-tier water journals, professional scientific code/software contributions, and high-quality datasets. Candidates must also be willing
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) at Oak Ridge National Laboratory (ORNL) is seeking a Postdoctoral Research Associate to perform R&D in the areas of electromagnetic transient (EMT) simulation and software development as well as dynamic
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to optimize utility of captured signals Conceive, write, and submit proposals to develop and expand a research program investigating signal collection and analysis for mission objectives Qualifications: A PhD
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analysis software. The prospective candidate will also have the opportunity to develop their own science that will complement the proposed DIB studies. Major Duties/Responsibilities: Study droplet interface
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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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on designing system software for automating processes such as intelligent data ingestion, preservation of data/metadata relationships, and distributed optimization of machine learning workflows. Collaborating