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-on experience in one or more of the following technology areas: hardware/software co-design, performance optimization with heterogeneous and alternative computing systems (CPU/GPU/NPU/etc.), FPGA design, high
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enterprise networking switches (Mellanox/Nvidia or Cisco preferred) Experience with Al/ML frameworks and GPU-based computing is highly desirable. Ability to work independently and as part of a team, with
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database modeling techniques (e.g., vector and graph databases). Familiarity with high-performance computing environments and AI-focused GPU provisioning. Experience with Microsoft CoPilot, OpenAI APIs, and
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well as the newly launched Center for Generative AI and its associated GPU cluster consisting of 600 GH200 nodes. Qualifications All candidates must hold a Ph.D. or equivalent degree in Chemistry, Molecular Biology
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learning, AI engineering, AI infrastructure, hybrid cloud computing, and parallel programming with GPUs, to work at the Institute for Artificial Intelligence and Data Science (IAD). As a Senior Research
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, hybrid cloud computing, and parallel programming with GPUs. As a Junior Research Engineer, you should have some basic understanding and experience in the development of scalable AI systems and deployment
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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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for the Foundations of Machine Learning (IFML), and Good Systems initiative as well as the newly launched Center for Generative AI and its associated GPU cluster consisting of 600 GH200 nodes. All positions are subject
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on heterogeneous processor types Optimized GPU computing and exploitation of GPU architectures for HPEC (tensors, multi-GPU instantiations, advances in GPU for AI/ML) Compute-focused optimization of System-on-Chip
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descriptive and appropriately detailed manner that can be understood by both researchers and future Research Software Engineers. Serve as liaison with Princeton Research Computing staff on GPU cluster related