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on high performance computing systems or cloud infrastructure (including GPU-accelerated workloads). Practical experience with modern deep learning frameworks, model serving in production, and building end
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their efforts on the education of students and the performance of life-changing research across a wide range of disciplines including medicine, engineering, physical sciences, energy, computer science, and social
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. Experience with graph-based data analysis or anomaly detection methods. Exposure to high-performance or GPU-based computing environments. Demonstrated ability to contribute to publications or technical reports
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algebra methods targeting large-scale HPC systems. Optimization of linear algebra libraries for modern architectures (e.g., GPUs). Exploration of linear algebra methods in computational physics applications
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, United States of America [map ] Subject Area: Computational Science / Artificial Intelligence/Machine Learning Appl Deadline: (posted 2025/11/19, listed until 2026/01/26) Position Description: Apply Position Description
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Researcher (R3) Country Morocco Application Deadline 11 Jan 2026 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU
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disease insights. The lab has state-of-the-art computing capabilities with an in-house cluster serving 80 CPU cores and 1.5TB of RAM, as well as a newly acquired NVIDIA DGX box with eight H100 GPUs and 224
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to roll out an advisory track in Computer Engineering, we are seeking qualified experts in Computer Networks, Computer Organization, Data Analytics, Cyber Security and Privacy, Edge/Fog/GPU Computing
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compute hardware and network infrastructure for the ITS Research services. This covers the 12000+ core, 100+ GPU QM High Performance Compute cluster (see https://docs.hpc.qmul.ac.uk ), various hosted
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: Collaboration: Collaboration and cross-fertilization between labs are strongly encouraged. HPC & AI Supercomputing Resources: An extensive GPU-based accelerated computing facility and high-performance data