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Field
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-of-the-art high-performance computing (HPC) infrastructure. In this position, you'll leverage your expertise to support and manage CSHL's AI-driven compute cluster powered by NVIDIA H100 GPUs, empowering
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especially on GPU infrastructure enhancements and improvements as part of Yale’s comprehensive campus investment in AI . As an experienced subject matter expert, you will help lead the system design
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Mathematica and Python with an interest in GPU programming. These required and desired skills should be demonstrated by presenting an existing body of code and/or peer-reviewed publications. Additional
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, postdocs, and graduate students. Fellows will have access to the AI Lab GPU cluster (300 H100s). Ideal candidates will have a strong interest and proven experience in designing, understanding, or engineering
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computing (HPC) systems, including CPU, GPU, storage, file systems, networking, visualization, job schedulers, and scientific applications Experience leading the implementation and execution of research
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(CNNs, vision transformers), multi-GPU multi-node training, and federated learning strongly encouraged. Experience working with cloud platforms. Experience with high performance computing. Publication
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). Experience managing systems utilizing GPU (NVIDIA and AMD) clusters for AI/ML and/or image processing. Knowledge of networking fundamentals including TCP/IP, traffic analysis, common protocols, and network
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, Probabilistic Inference, Algebraic Topology and Wavelet analysis theory. Familiar with Matlab/Python/C++ programming. Experience with Pytorch and multi-GPU model deployment. Experience in analyzing complex
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software development with GPUs. Experience with visualization of large data sets. An understanding of how to make results easily available using common web interfaces. Required Documents Resume Cover Letter
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. Computational Infrastructure: Deploy and maintain high-performance computing environments (GPU clusters, cloud services) for large-scale image-text experimentation. Data Engineering: Establish workflows