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Responsibilities: Study and analyze dataflow patterns in emerging data-intensive applications. Design and develop parallel file systems for node-local NVMe. Evaluate the performance implications of alternative
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-motivation skills Strong foundation in CFD with proficiency in Python and AI/ML techniques, and additional experience in parallel computing tools such as CUDA and MPI Experienced with CFD simulation tools (e.g
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Ability to code in a programming language to solve computational problems Awareness of computational infrastructure and its upkeep Ability to work on multiple projects in parallel and set priorities
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parallel clusters Integrate existing physical models into new software infrastructure for EOS research Benchmark against existing methods and support reproducible, open-science practices Collaborate closely
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Strong foundation in CFD, Programming proficiency such as Python, AI/ML techniques, Experience with parallel computing on CPU/GPU cluster, use of CUDA, MPI is a plus. Experience Experience with open-source
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for multiple-CPU and/or GPU platforms via parallelization schemes. Validating these codes via canonical and real-world examples. Job Requirements: PhD in Electrical and Electronic Engineering, Applied
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model is employed to forecast renewable energy availability, providing crucial insights for the design optimization process. The ML-assisted operation tackles the dynamic optimization of parallel energy