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Field
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concepts to project teams with varying levels of expertise. Serve as a liaison with Princeton Research Computing staff on GPU cluster-related issues. Application of Domain Expertise Collaborate with our
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computational infrastructure such as A100 and H100 GPUs, combined with pre-processed large-scale biobank data such as UK Biobank and ADSP, enabling you to work at the scale required for breakthrough research
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structured and unstructured datasets, and GPU-accelerated computing. Proven experience with Large Language Models. Required Skill/Ability 3: Sound background in theoretical and applied machine learning/deep
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in GPU programming one or more parallel computing models, including SYCL, CUDA, HIP, or OpenMP Experience with scientific computing and software development on HPC systems Ability to conduct
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high-performance computing capabilities (including approximately 4,000 Nvidia RTX 4000 Ada GPUs and over 30,000 CPU cores) hosted at the project data center in Nevada where the telescope is located
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for performance, cost-efficiency, and low-latency inference Develop distributed model training and inference architectures leveraging GPU-based compute resources Implement server-less and containerized solutions
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. Experience with algorithm design, embedded DSP development, multithreaded programming, GPU development, SDR hardware platforms, FPGA development, and/or Linux-based designed tools is desired. Representative
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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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containing plasma in fusion reactors, revolutionizing material design and structure, novel electronics and bioengineering applications. AI Postdoctoral Research Fellows will have access to the AI Lab GPU
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inference Develop distributed model training and inference architectures leveraging GPU-based compute resources Implement server-less and containerized solutions using Docker, Kubernetes, and cloud-native