36 software-defined-networking Postdoctoral positions at Oak Ridge National Laboratory
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advanced computational approaches, software engineering, high-performance computing, and an understanding of materials science, with an emphasis on metallurgy related to phase transformations and/or
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, Neutron Sciences Directorate at Oak Ridge National Laboratory (ORNL). The qualified candidate will study, simulate and develop software for beam transport and beam dynamics in SNS superconducting linac
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other MEERA Group industrial technical deployment (Better Plants) and Energy System Software Tools development projects. Help support the development of new resources, trainings, and tools to support
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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
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development techniques (numerical methods, solution algorithms, programming models, and software) at scale (large processor/node counts). Experience with use of artificial intelligence and machine learning in
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, heterogeneity-aware scheduling, robust and efficient training and fine tuning. Contribute to open-source software, datasets, and standardized evaluation suites; mentor interns and students. Communicate results
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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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to cutting-edge research aimed at transforming scientific data management and workflows to enable AI-readiness at scale. You will work on designing system software for automating processes such as intelligent
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workflows to enable AI-readiness at scale. You will work on designing system software for automating processes such as intelligent data ingestion, preservation of data/metadata relationships, and distributed
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, Scikit Learn, etc., in applied problem-solving contexts. Understanding of machine learning algorithms (gradient descent, random forests, etc.) and deep neural network architectures (Transformers). A broad