246 parallel-and-distributed-computing-phd-"Meta"-"Meta" positions at Oak Ridge National Laboratory
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of sparse matrix, tensor and graph algorithms on distributed and heterogenouscomputational environments. Basic Qualifications: A PhD in Computer Science, Applied Mathematics, Computational Science, or related
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; establish capabilities that enable the SNS user program to be successful and incorporate distributed control system best practices. Major Duties/Responsibilities: Establish a vision to successfully create and
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ORNL and ESED project’s quality assurance requirements. The successful candidate will be responsible for the coordination of division documents through review, approval, and distribution with additional
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degree in computer science, engineering or related field with at least 8 years of applicable experience; master’s degree with at least 7 years of applicable experience; or PhD with at least 4 years of
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communities. You will work closely with a focused group of scientists, technicians, and engineers on ambitious, fast paced technical projects in support of the DOE Isotope Program. This has been a quickly
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in the Emerging Technologies, AI & Computing group in the Research Computing Support division in the Information Technology Services Directorate at Oak Ridge National Laboratory (ORNL). This role
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utility production and distribution systems that provide utilities for buildings and facilities at UT-Battelle as well as other tasks as assigned my management. Utility systems include potable and process
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specialized diagnostics, particularly for high dynamic range and high dimensional measurement of particle beam distributions. This project is motivated by the limitation presented by uncontrolled beam loss
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specialized diagnostics, particularly for high dynamic range and high dimensional measurement of particle beam distributions. This project is motivated by the limitation presented by uncontrolled beam loss
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, MATLAB, Git, debugging, and modern software engineering practices. Experience with GPU computing (e.g., CUDA, HIP), parallel computing (e.g., MPI, Actor Model). Familiarity with containerization (e.g