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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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. 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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, 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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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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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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vision systems (e.g., NVIDIA Jetson Nano) Real-time processing and GPU acceleration Experience working on industry R&D projects Key Competencies Able to build and maintain strong working relationships with
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recent architectures such as vision transformer or foundation models Experience in working with subsurface imaging Proficiency in leveraging GPUs and distributed training for large-scale datasets is highly