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hardware architects to establish how agentic AI and these languages co‑design with heterogeneous HPC systems (CPUs, GPUs, PIM, AI accelerators). Study performance and portability tradeoffs, leveraging
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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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. 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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, optimizing, and deploying AI models on HPC and GPU-based systems. Provide guidance on performance optimization, scaling, and efficient resource utilization. Contribute to architectural and design decisions in
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agentic workflow (Llama, Claude, LangGraph, etc) Experience with training models on GPUs Experience working with Unix and/or Linux, including shell scripting Experience implementing NLP/AI algorithms and
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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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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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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
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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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IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava | Czech | 14 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