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algebra methods targeting large-scale HPC systems. Optimization of linear algebra libraries for modern architectures (e.g., GPUs). Exploration of linear algebra methods in computational physics applications
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, and progression outcomes) and high-end compute (hundreds of NVIDIA H100 GPUs) via Mila and the Digital Research Alliance of Canada, and involves active collaborations with Stanford, Oxford, Google
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(Dell Precision 7960 Tower with NVIDIA RTX 6000 GPU, 128GB RAM, 32-core CPU) for large-scale NLP and machine learning experiments. The planned start date is 15 November 2025 or as soon as possible after
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. Desirable criteria Experience working with generative models or large language models Experience with GPU-based model training or cloud computing Knowledge of synthetic biology or regulatory sequence design
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(e.g. systems biology), or ordinary/stochastic differential equations. Experience in computational, statistical, or machine learning method development in any discipline. Experience in GPU computing
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animation tools, and GPU-based high-performance computing at MPI. You will also be embedded in a rich theoretical and computational environment supported by the Multimodal Language Department.Requirements
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animation tools, and GPU-based high-performance computing at MPI. You will also be embedded in a rich theoretical and computational environment supported by the Multimodal Language Department.Requirements
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results. Machine Learning skills to automise comparison process. Unbiased approach to different theoretical models. Experience in HPC system usage and parallel/distributed computing. Knowledge in GPU-based
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and planet formation context Experience in the field with HPC system usage and parallel/distributed computing Knowledge in GPU-based programming would be considered an asset Proven record in publication
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opportunities, access to modern GPU clusters for deep learning research, and strong academic-industry connections. CADIA's commitment to open science aligns perfectly with this project's goals of creating