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or streaming data. Develop parallelized and GPU-accelerated learning modules, ensuring scalability and performance efficiency. Build and maintain robust data pipelines for high-throughput modeling over
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 1 day ago
hands-on access to Australia’s national supercomputing infrastructure—including world-class HPC clusters, large-scale GPU systems, cloud platforms, and high-performance file systems. You’ll support
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systems, and the Zero-G Lab, a unique facility designed to emulate proximity operations under space-like conditions. In addition, CVI2 provides high-performance GPU computing resources that support the
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provides high-performance GPU computing resources that support the design and training of advanced AI models. The research agenda of CVI2 focuses on cutting-edge topics such as 3D understanding and
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adaptive optimization during needle insertion, integrating live ultrasound imaging with GPU-accelerated dose calculation and optimization. The Postdoctoral Research Associate will join a multidisciplinary
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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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or streaming data. Develop parallelized and GPU-accelerated learning modules, ensuring scalability and performance efficiency. Build and maintain robust data pipelines for high-throughput modeling over
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 4 days ago
that supports this project has an expected end date of 30 June 2028. This role gives you hands-on access to Australia’s national supercomputing infrastructure—including world-class HPC clusters, large-scale GPU
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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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growing GPU cluster containing thousands of high-end GPUs. Depending on the day, we might be diving deep into market data, tuning hyperparameters, debugging distributed training performance, or studying how