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to closely collaborate with CS faculty members Sriram Pemmaraju and Sourya Roy on sampling problems in the distributed and parallel computing setting. The ideal candidate will have research experience
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Center (HT- RDS Center). The applicant would be responsible for designing and developing workflows for automated, micro-scale parallel experimentation. These workflows will be used for the
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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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Lab researches on a variety of computer systems topics including HPC resilience, data center power management, large-scale job scheduling and performance tuning, parallel storage systems and
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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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postdoctoral appointee to develop computational fluid dynamic (CFD) tools that make exascale computing accessible to a broader set of users. The successful candidate will develop a massively 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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develop both high-throughput and gene-specific approaches for determining the functional consequences of human genetic variation. Current and developing projects include massively parallel splicing
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single-mode and multimode semiconductor laser arrays Optimization and acceleration of existing simulation codes Parallel computing and high-performance computing (HPC) GPU acceleration, where