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). Preferred Qualifications: Familiarity with advanced storage solutions and parallel file systems (e.g., Lustre, GPFS, or BeeGFS). Advanced knowledge of Linux operating systems, networked storage environments
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algorithms and parallel/distributed variational algorithms in AI/ML for application workflows and large-scale HPC and QC systems Develop quantum machine learning (QML) algorithms for optimization of multi
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, Git, Azure Data Studio, pandas, NumPy, and Scikit-learn. Experience with High-Performance Computing (HPC) platforms with advanced skills in SLURM-based multi-node, multi-GPU training, data parallelism
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working with Infiniband and parallel filesystems (Lustre, GPFS) is a plus. Experience managing Linux/UNIX operating systems in a heterogeneous environment. Solid understanding of networked computing
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diagnostics. Experience with Infiniband networks and diagnostics. Extensive experience with High Performance Parallel File Systems (Lustre, WEKA, GPFS, etc). Experience with performance and diagnostic tools
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, MATLAB, Git, debugging, and modern software engineering practices. Experience with GPU computing (e.g., CUDA, HIP), parallel computing (e.g., MPI, Actor Model). Familiarity with containerization (e.g
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. Extensive experience with High Performance Parallel File Systems (Lustre, WEKA, GPFS, etc). Experience with performance and diagnostic tools for benchmarking, analysis and tuning of systems, networking, and