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accelerators Hardware–software co-design for AI and transformer-based models. Responsibilities Conduct cutting-edge research in parallel computing and AI accelerators Develop and lead externally funded projects
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or OpenMP. Experience in heterogeneous programming (i.e., GPU programming) and/or developing, debugging, and profiling massively parallel codes. Experience with using high performance computing (HPC
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programming proficiency in Python and C/C++. Expertise in ensemble learning (e.g., Random Forests, Gradient Boosting, bagging/stacking frameworks). Hands-on experience with parallel or GPU-based computing (CUDA
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-edge Machine Learning applications on the Exascale computer JUPITER. Your work will include: Developing, implementing, and refining ML techniques suited for the largest scale Parallelizing model training
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, graduate and undergraduate students, and external collaborators. Skills in parallel computing, parallel application design and development, and performance evaluation and optimization. Knowledge and
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) for a given Tiramisu program, many code optimizations should be applied. Optimizations include vectorization (using hardware vector instructions), parallelization (running loop iterations in parallel
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) who will develop computational electromagnetics codes for rapid characterization of the fields scattered from artificial metasurfaces. Key Responsibilities: The key responsibilities of the Research
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Science. The research focus of our group is on parallel computing, supercomputing, and performance tuning and optimization of advanced applications. Our team currently consists of 10 scientific and 3 administrative
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, parallel storage systems and scientific data management. Recent research project details and outcomes can be found in computer systems conference proceedings, such as HPCA, FAST, SC, DSN, HPDC, IPDPS, and
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groups settings. Ability to coordinate with faculty, graduate and undergraduate students, and external collaborators. Skills in parallel computing, parallel application design and development, and