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pipelines, and rigorously quantify reductions in energy per solve compared with optimized CPU/GPU and FPGA baselines. The project targets three real THz-NDE use cases: (i) sparse deconvolution of THz impulse
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models on GPU infrastructure (SSH access) and distributed computing environments. Strong problem-solving, documentation, and communication skills across technical and non-technical contexts. Ability
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develop new features and extend the capabilities of a real-time neural data processing and decoding platform. This includes optimizing GPU-accelerated signal processing pipelines, improving system
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Our org owns Meta's hardware tech strategy for AI - finding innovative hardware for GPUs and Meta's custom AI chips, as well as CPU, memory, and storage as well as getting these to work in Meta's
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or TensorFlow. Practical background in training and validating models on GPU-based and distributed computing environments. Working knowledge of containerization tools and orchestration platforms (e.g. Docker
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datasets and run experiments on HPC infrastructure (GPU clusters, SLURM). Strong written communication skills for technical documentation, reporting, and research outputs. Ability to work independently and
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tuned for thermal transport and fire dynamics. FDS has been scaled up to 10,000 cores on Titan. The aim of this project is to improve code performance for serial, OpenMP, MPI, and potentially GPU
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Workshops (INFOCOM WKSHPS), 2021, pp. 1–6. [4] W. Gao, Q. Hu, Z. Ye, P. Sun, X. Wang, Y. Luo, T. Zhang, and Y. Wen, “Deep learning workload scheduling in gpu datacenters: Taxonomy, challenges and vision
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Information Benefits Trabajo en IA generativa de vanguardia aplicada al habla / Work on cutting-edge generative AI for speech Acceso a servidores GPU y recursos de cómputo / Access to GPU servers and computing
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them. Research Computing & AI Enablement - Work with the Architecture team to build scalable HPC and GPU-enabled environments on IaaS cloud sites along with other specialized hosted solutions. Work