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Field
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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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on small test clusters. Test computational performance and resolve technical challenges on significantly larger models of selected quantum materials. Work on speeding up Krylov solvers on GPUs. Demonstrate
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 1 month 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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platforms and our local CPU and GPU clusters; implementing Python tools for automating CSP/DFT calculations; - Participation in the scientific activities of the Applied Quantum Chemistry group (IC2MP) and the
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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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. Qualifications: Familiarity with machine learning interatomic potentials, CPU and GPU parallelization, knowledge of LAMMPS and molecular dynamics, experience with first principles calculations of dielectric and
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Experience with HPC (GPUs preferred) Related Skills and Other Requirements Ability to work at the interface of AI and science/engineering problems Ability to lead, develop, and contribute to multiple projects
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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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, engineering, physical science or related technical discipline. Experience: Expertise in developing and training AI models Proficiency in Python Experience with HPC (GPUs preferred) Related Skills and Other
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of cores, and a 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