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statistical mechanical theory to analyze the simulation results Basic understanding of parallel application development techniques (parallel programming models, algorithms, and software) Preferred
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commercial CFD codes such as STAR-CCM+, ANSYS or COMSOL. Knowledge of finite element simulations methods. Experience using parallel Linux computing platforms, parallel job submission scripts, common
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tensor network algorithm development and application. Competency with common scientific programming languages such as C++, Python, and/or Julia and version control systems. Experience in parallel
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massively parallel computing systems, including GPU-accelerated platforms. Develop and maintain scientific software, primarily in Python , using modern practices for software development, testing
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areas include: Parallel and distributed graph and or ML algorithms — shortest paths, connectivity, clustering, or graph traversal on GPU clusters and distributed-memory systems. Sparse direct and
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on high-performance computing (HPC) environments using distributed data or model parallel training. Experience with multi-physics simulations on HPC and with ML models. Experience working in a multi
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methods through which AI agents guide architectural design decisions for massively parallel heterogeneous systems, including pathways that integrate quantum or analog co-processors. Performance
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methods through which AI agents guide architectural design decisions for massively parallel heterogeneous systems, including pathways that integrate quantum or analog co-processors. Performance
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systems techniques. Proficiency in programming languages such as Python, C++, or similar, as well as experience with HPC environments and parallel computing. Demonstrated hands-on experience and
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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