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Field
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including code design, documentation and testing. Familiarity with optimization methods including Machine Learning (ML) techniques. Any experience with computations on GPUs. Working knowledge of Linux command
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projects to the Tier 2 supercomputer Bede (32 IBM Power 9 dual-CPU nodes, each with 4 NVIDIA V100 GPUs and high performance interconnect). The Bourne-Worster group is also well-provisioned with
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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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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
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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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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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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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libraries for modern architectures (e.g., GPUs). Exploration of linear algebra methods in computational physics applications and machine learning. Integrate and benchmark the GINGKO library, a sparse solver
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datasets generated by the Phenomobile.v2+ to identify key traits affecting crop performance under stress conditions. Implementing a multimodal approach for large-scale data analysis using CPU and GPU