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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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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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, 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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. 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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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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IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava | Czech | 11 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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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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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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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