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field or method, including, but not limited to, numerical methods, machine learning, or parallel and distributed computing. Expertise in a parallelization method (e.g., CUDA or ROCm, MPI, OpenMP
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media, news media, digital signage, podcast, signature events, and executive presentations. This candidate must be comfortable managing multiple projects in parallel, many of which require the execution
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and lead in the development of compute resources critical to CESR operations, analysis, and simulation – including the CLASSE Compute farm which supports batch, parallel, GPU, and interactive workflows
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innovation into practice. SDSC adopts and partners on innovations in industry and academia in the areas of software, hardware, computational and data sciences, and related areas, and translates them
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. Documents requirements, defines scope and objectives, and formulates systems to parallel overall business strategies. Responsibilities Job Responsibilities: 1. Assists with program planning and implementation
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departmental website (Drupal.) 4. Support discipline specific software applications (AutoCAD, Powermill, distributed computing management, Rhino, Maya, Maxwell, and Revit); in addition, provide support for Yale
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programming distributed systems; Experience with parallel and distributed File Systems (e.g., Lustre, GPFS, Ceph) development. Advanced experience with high-performance computing and/or large-scale data centers
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algorithmic paradigms, such as machine learning algorithms, parallel algorithms, and distributed systems. Relevant certifications (e.g., Data Structures and Algorithms Specialization from Coursera, Google IT
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concepts, such as graph theory, greedy algorithms, divide and conquer, and backtracking. Knowledge of modern algorithmic paradigms, such as machine learning algorithms, parallel algorithms, and distributed
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data analysis and visualization. The faculty member’s research program is expected to develop and incorporate novel algorithms and frameworks, such as deep learning, parallel and distributed computing