80 parallel-programming-"LCC-CNRS" Postdoctoral positions at Oak Ridge National Laboratory
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understanding of parallel application development techniques (parallel programming models, algorithms, and software) Preferred Qualifications: Experience in implementing ab initio simulation codes such as VASP
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Requisition Id 16505 Overview: The Computer Science and Mathematics Division at Oak Ridge National Laboratory (ORNL) is seeking an exceptional Postdoctoral Researcher to advance programming systems
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represent the organization by giving technical presentations to DOE program managers and in large public forums. Basic Qualifications: A PhD or equivalent experience in nuclear, mechanical, or chemical
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common scientific programming languages such as C++, Python, and/or Julia and version control systems. Experience in parallel programming with one or more common parallel programming models, like MPI
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. Experience with heterogeneous and parallel computing technologies, programming models, or accelerators, including CPUs, GPUs, FPGAs, and emerging computing technologies. Familiarity with quantum software
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techniques (parallel programming models, algorithms, and software) Experience in theoretical and computational physics or material sciences Preferred Qualifications: Background in the development
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-reviewed journals and conferences Demonstrated research experience with HPC, AI/ML and/or distributed systems techniques. Proficiency in programming languages such as Python, C++, or similar, as
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include the porting of lattice QCD libraries to the Exascale Architectures (e.g. Frontier at OLCF) and/or emerging programming models (HIP, SYCL, Kokkos, etc), software optimization and/or algorithmic
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efforts supported by the U.S. Department of Energy Office of Science, including the Advanced Scientific Computing Research (ASCR) program and the Genesis initiative. These programs focus on integrating AI
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Language Models (LLMs). Distributed Machine Learning: Specialization in data parallelism, model-parallelism, and collective communication strategies in large-scale environments. Proficiency in frameworks