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to begin September 1, 2025. We will consider strong candidates in any research area but will prioritize Distributed and Parallel Computing. A PhD in computer science or a related area is required
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optimize large-scale distributed training frameworks (e.g., data parallelism, tensor parallelism, pipeline parallelism). Develop high-performance inference engines, improving latency, throughput, and memory
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increasingly complex compute- and data-intensive problems in science and engineering on high-end parallel and distributed computing platforms. More information: https://sc.cs.univie.ac.at/ Your future tasks
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 hours ago
problem-solving skills and the ability to work in a collaborative environment. Preferred Qualifications, Competencies, and Experience Distributed parallel training and parameter-efficient tuning
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evaluation, or a strong willingness to gain hands-on experience. Eagerness to learn HPC concepts, including parallel computing, distributed systems, and optimization. Analytical skills, problem-solving
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We are seeking an experienced and enthusiastic Cloud Engineer to be based at the Institute of Astronomy (www.ast.cam.ac.uk ) in collaboration with the Research Computing Services (www.hpc.cam.ac.uk
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, and digital forensics Programming languages and distributed and parallel computing Data science and machine learning EEO Statement: Hofstra University is an equal opportunity employer and is committed
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Your Job: Agile development, maintenance, coordination, testing, distribution and deployment of the open-source, community-driven Elephant neural data analysis software - https://python-elephant.org
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toolchains Familiarity with distributed training/inference, AI system bottlenecks, and performance tuning Prior experience with cloud computing and AI system deployment in production settings
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University Apple devices to be parallel to all University Windows devices. 15/202 6. Assists with the specification, development, documentation, deployment, and maintenance of the staff, faculty and computer