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/GPUs. These devices provide massive spatial parallelism and are well-suited for dataflow programming paradigms. However, optimizing and porting code efficiently to these architectures remains a key
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applied Machine Learning Hands-on experience with High Performance Computing Systems Basic knowledge of System Architecture of Supercomputers and NVidia-GPUs Practical experience with ML/DL workflows and
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IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava | Czech | 15 days ago
international projects, · collaboration with application developers and domain experts on highly scalable parallel applications with focus on: - development and implementation of parallel aplications, - GPU
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artificielle (IA) (CPU, GPU, accélérateurs d'IA, etc.) nécessitent une puissance élevée et des réseaux de distribution d'énergie (PDN) optimisés pour améliorer l'efficacité en puissance et préserver son
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scientific computing. You are proficient in several languages (Python, C/C++, or Fortran), with extensive knowledge in AI/ML and parallel programming (GPU, multi-threading, etc.). You have strong software
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distributed computing for EMT simulations. • Experience with software development in Python, C++, or other programming languages. • Familiarity with GPU acceleration of numerical solvers, parallel sparse
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) platforms used in machine learning, big data and artificial intelligence (AI) based applications (CPUs, GPUs, AI accelerators etc.) require high power demands with optimized power distribution networks (PDNs
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(HPC) platforms used in machine learning, big data and artificial intelligence (AI) based applications (CPUs, GPUs, AI accelerators etc.) require high power demands with optimized power distribution