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of today’s heterogeneous hardware (multicore CPUs, GPUs, SmartNICs, disaggregated datacenters). We explore: SmartNICs & P4 switches for offloading intelligence from hosts Device-to-device communication
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part of the core PLI team, which includes top-tier faculty, research fellows, scientists, software engineers, postdocs, and graduate students. Fellows will have access to the AI Lab GPU cluster (300
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, research fellows, scientists, software engineers, 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
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made at the Postdoctoral Research Associate rank. The AI Postdoctoral Research Fellow will have access to the AI Lab GPU cluster (300 H100s). Candidates should have recently received or be about to
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energy efficiency bounds of modern CPU, GPU and FPGA devices at performing set operations in the context of combinatorial applications; Investigation of current trends in programming FPGA accelerators and
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, energy consumption, and accuracy.; ; Training deep learning models, especially in LLMs, faces critical challenges that compromise the optimal use of GPUs. These bottlenecks result in poor computational
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. Key attractions are access to a high-performance computing cluster (GPU/CPU and more than 300TB of data), two 3T Prisma MR scanners, and an MR compatible digital EEG system as well as collaboration
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GPU clusters to enhance efficiency and scalability. Knowledge, Skills, and Abilities: Good communication and teamwork skills; Strong skill in large language model customization techniques including
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these codes in C++ or Fortran Adopting these codes for multiple-CPU and/or GPU platforms via parallelization schemes. Validating these codes via canonical and real-world examples. Job Requirements: PhD in
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. Ability to succeed in an interdisciplinary team and communicate results clearly in writing and presentations. Desired Qualifications: Knowledge of GPU architecture and GPU programming. Interest