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Field
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highly-motivated candidate, skilled in numerical model development (programming in FORTRAN, numerical methods, HPC environment). Experience in numerical methods on unstructured grids and/or GPU would be
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of predicting electronic, structural, and thermal quantities while leveraging underlying symmetries for computational efficiency. There will be a significant computational component in deploying multi-GPU codes
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scalability of simulation workflows via: Parallelization and performance engineering GPU/accelerator optimization Algorithmic innovation Experience applying machine learning or AI to molecular simulation
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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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academic setting Proficient in at least two programming languages used in research (e.g. Python, C++, Fortran) Experience with programming paradigms used in HPC (e.g., MPI, GPU-programming) Experience
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 2 months 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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. Conduct experimental studies using GPU-enabled computing resources for model training, inference, and simulation-based evaluation. Support rapid prototyping and iteration of research ideas, from concept
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optimized code written by expert programmers and can target different hardware architectures (multicore, GPUs, FPGAs, and distributed machines). In order to have the best performance (fastest execution) for a