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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 10 days ago
of complex AI research workloads on state-of-the-art hardware. The role will have heavy focus on optimizing existing NVIDIA GPU-based workloads for top-tier AMD GPUs, such as MI355X and beyond and will analyze
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performance of complex AI research workloads on state-of-the-art hardware. The role will have heavy focus on optimizing existing NVIDIA GPU-based workloads for top-tier AMD GPUs, such as MI355X and beyond and
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well as large-scale GPU computing facilities for deep learning. We are looking for a Research Engineer to manage the EEE GPU Cluster. The role will focus on enhancing the EEE GPU Cluster team’s ability in terms
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différents types de parallélisme, que ce soit au niveau d'un nœud de calcul (CPU et GPU) qu'au niveau d'une grappe de PC. Cet environnement inclura les outils nécessaires à la description et à la construction
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/GPU environments. Provide consultative support and training to researchers using BRC AI/ML tools and pipelines. Performs related duties & responsibilities as assigned/requested. Qualifications REQUIRED
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FLAME-GPU accelerated agent-based modelling of material response to environmental and operational loading EPSRC CDT in Developing National Capability for Materials 4.0, with the Henry Royce
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Language Model (LLM) GPU cluster to ensure stable and reliable operation of training tasks; (b) handle GPU node failures, IB network anomalies, CUDA/NCCL errors and Kubernetes scheduling failures, perform
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Language Model (LLM) training platform, developing unified capabilities for GPU resource pooling, training job scheduling, inference acceleration and the Machine Learning Operations (MLOps) platform
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Job Code 0005 Employee Class Civil Service Add to My Favorite Jobs Email this Job About the Job The successful applicant will assist in the adaptation of the PPMstar code to run well on GPU-accelerated
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Max Planck Institute for Radio Astronomy, Bonn | Bonn, Nordrhein Westfalen | Germany | about 5 hours ago
analysis systems using GPU- and FPGA-supported HPC clusters at large international research facilities such as Effelsberg, SKA, and MeerKAT. The systems developed by the BDG are based on state-of-the-art