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implement ensemble learning algorithms and optimization strategies for large-scale or streaming data. Develop parallelized and GPU-accelerated learning modules, ensuring scalability and performance efficiency
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. The Interpretable Machine Learning Lab has dedicated access to high-performance CPU and GPU computing resources provided by Duke University’s Research Computing unit and state-of-the-art IT infrastructure. Ideal
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. Expertise with AWS CPU and GPU computing, cloud computing, AI model architecture, training, and validation, reinforcement learning, generative artificial intelligence, and data assimilation in an industry and
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lead in designing, deploying, enhancing, and managing our HPC infrastructure. This infrastructure includes GPU clusters (B200/H200/A40), liquid-cooled CPU cluster, and a cloud-based HPC system, with
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that combine parallel architectures (i.e., GPUs or accelerator boards, clusters) and numerical algorithms suited to such architectures with the goal of improving the speed of convergence and the stability
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one or more of the following areas: Programming, Algorithms and computational complexity, Programming languages and compilers, Computer graphics, and Parallel and GPU computing. A typical teaching
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. (Experience may include time while doing graduate studies.) Experience with GPU programming for scientific/engineering computations. Experience using containerization software (such as docker or apptainer) Two
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managing experiments using GPUs Ability to visualize experimental results and learning curves Effective inter-personal and team-building skills Self-motivated with an ability to work independently and in a
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atmospheric modeling, artificial intelligence, and near real-time applications. The successful candidates will contribute to the development of high-performance and GPU-enabled modeling tools for wildfire
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infrastructure, model training, and inference systems. You'll design, develop, and optimize scalable data pipelines and build multi-node GPU training and inference pipelines for foundational models. You'll also