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programming (Python, C++, etc.) and machine learning and signal processing libraries; You have HPC/GPU computing experience, including running deep learning workloads on compute clusters (CUDA-compatible GPUs
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, informatics, physics or a related field strong expertise in machine learning strong interest in high performance computing on CPUs and GPUs proficiency in Fortran, Python, shell scripting proficiency with Linux
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IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava | Czech | about 12 hours ago
deployment, · knowledge of GPU computing and large-scale training, · experience working in an HPC environment, · experience with data annotation pipelines or synthetic data generation. We offer: · work in a
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IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava | Czech | 3 days ago
deployment, · knowledge of GPU computing and large-scale training, · experience working in an HPC environment, · experience with data annotation pipelines or synthetic data generation. We offer: · work in a
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manufacturing. Your work will capture compressible gas dynamics, heat transfer, free-surface/melt behaviour, and mass transfer driven by phase change within a GPU-accelerated solver to reduce simulation
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learning and signal processing libraries; You have HPC/GPU computing experience, including running deep learning workloads on compute clusters (CUDA-compatible GPUs, multi-GPU training, Slurm). Your master's
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funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The PhD student will be
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We are seeking a highly motivated PhD student to perform fundamental research and to conceive truly sparse solutions (on both, CPU and GPU) for dynamic sparse training, aiming to cut the training
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/ computer vision and pattern recognition, including but not limited to biomedical applications Strong interest in applied machine learning, including but not limited to deep learning Experience utilising GPU
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of spikes by a model Develop proxy apps representing the different processing stages of spiking network simulation code (targeting CPU and accelerators such as GPU or IPU) Systematic benchmarking of proxy