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Field
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software, including interactive desktop plugins and web-based analysis tools where appropriate. Optimize workflows for large, multidimensional datasets and GPU or parallel computing. Expand annotated
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; and one-sided asynchronous programming models. Experience in heterogeneous computing, developing and debugging massively parallel algorithms and code performance profiling are a plus. Special
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time-series data Experience of high-performance computing, GPU-based computing, or parallel programming Experience of probabilistic modelling or inverse problems Experience of developing and
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The Center for Advanced Computing (CAC) at Cornell University seeks a senior systems administrator to design, deploy, and manage high performance computing (HPC) clusters and servers. Well
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systems for extreme-scale, heterogeneous computing. This role sits at the intersection of compiler infrastructure, formal verification, runtime systems, and hardware/software co‑design—expanding into
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-performance computing (HPC), visualisation, and storage at Lund University. We provide advanced computing and data services to researchers across all faculties of the university, including science
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mechanics. Familiarity with earthquake physics, induced seismicity, geothermal energy, carbon storage, or subsurface engineering applications. Experience with parallel computing, scalable workflows
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are seeking an experienced security engineer who can bridge hands-on engineering and security governance to ensure the security, performance, and reliability of the NCCS computing infrastructure. Our
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destination. Job Summary The Data Science Institute (DSI) Tech staff is responsible for the design, operation, and security of all computing infrastructure within the Data Science Institute, including
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University of North Carolina at Chapel Hill | North Wilkesboro, North Carolina | United States | about 2 months ago
and his team to help manage the data application, storage and transfer, and package the developed tools for Linux/Mac/Windows platforms, accelerate the program with parallel computing techniques