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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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to decompose the task into generating individual 2D horizontal layers separately in order to save GPU memory resources. Your Qualifications / Experience: completed MSc university degree in mathematics
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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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IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava | Czech | about 10 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 | 2 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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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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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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: LightKrylov/LightROM libraries, Incompact3d solver, Pprime CPU/GPU infrastructure, IUSTI experimental database. 4- PRINCIPAL TASKS AND RESPONSIBILITIES ----------------------- a.Stability analyses: Perform
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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