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institution. • Experience in developing and training machine models using PyTorch or TensorFlow in Multi-GPU environments. • Experience in working with high performance computing systems for both training and
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software development. Experience with use of DSPs, FPGAs, advanced computing (e.g., GPUs, QPUs). Excellent written and oral communication skills. Motivated self-starter with the ability to work independently
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with multiple deployment mechanisms like Diskless, Warewulf, and traditional deployment (cobbler, PXEboot, and/or Bright). Experience managing systems utilizing GPU (NVIDIA and AMD) clusters for AI/ML
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computing (HPC), distributed compute, or accelerated environments (GPUs, multi-node systems). Solid background in database systems, including: Relational databases (e.g., PostgreSQL / SQL) Graph databases
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), including model training, evaluation, and experimentation. Familiarity with distributed or accelerated computing environments (e.g., GPU‑enabled systems, shared compute clusters). Working knowledge
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, evaluation, and experimentation. Experience working in high-performance computing (HPC), distributed compute, or accelerated environments (GPUs, multi-node systems). Solid background in database systems
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infrastructures, improving system performance, scalability, and efficiency by optimizing resource usage (e.g., GPUs, CPUs, energy consumption). Researchers and students will explore innovative approaches to reduce
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infrastructures Documented, long-term experience of independent research within life science/bioinformatics Documented experience of large-scale data management, high-performance computing (HPC), GPU acceleration
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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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into a cloud environment. Minimum two to three years of experience using PyTorch or Tensorflow, including optimizing code for GPU clusters Experience building advanced GenAI workflows such as retrieval