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multiple languages. Proficient on a computer cluster and ideally programming with GPU nodes. Preferred Competencies Knowledge of relevant scientific fields and procedures. Work both independently and in
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Linux kernel internals, computation accelerators (e.g., GPU computing, CUDA), MPI, and OpenMP. Highly resourceful and adept at juggling multiple simultaneous projects. Must demonstrate ability to work
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of servers, storage systems for multi-terabyte databases, backup system and specialized networks that enable research programs whose per annum budgets are in the tens of millions of dollars. Assist researchers
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several hundred nodes, various types of servers, storage systems for multi-terabyte databases, backup system and specialized networks that enable research programs whose per annum budgets are in the tens
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hardware architectures (multicore, GPUs, FPGAs, and distributed machines). In order to have the best performance (fastest execution) for a given Tiramisu program, many code optimizations should be applied
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-driven applications, machine learning, and artificial intelligence. A portion of this role will directly support the AI + Science Postdoctoral Program and PI/faculty collaborators on this program, which is
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generative artificial intelligence. In addition to impactful research, the project features an ambitious stakeholder engagement program designed to inspire young people to pursue STEM careers, increase public
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’ experience post-Ph.D. Expertise in power systems and power electronics. Experience in C/C++, Matlab, and Python programming. Experience in power systems software like PSCAD, PSS/e. Preferred Qualifications
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) general-purpose hardware such as accelerators for AI and ML, high-performance computing, low-power edge computing, quantum computing, cybersecurity, chiplets, and CPU, TPU, GPU, and FPGA systems; or (2
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knowledge, skills, and abilities Expert-level knowledge of the Python programming language. Expert-level knowledge of multiple modern AI/ML, vision, NLP, bioinformatics and/or mathematical or computational