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automated data pipelines for large-scale time-series or imaging datasets. Experience with HPC/cluster computing environments, including SLURM job scheduling and GPU-accelerated processing. Experience with
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-on experience in one or more of the following technology areas: hardware/software co-design, performance optimization with heterogeneous and alternative computing systems (CPU/GPU/NPU/etc.), FPGA design, high
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, or other highly regulated computing environments. HPC, AI/ML infrastructure, GPU computing, containers, Kubernetes, or advanced computing platforms. Infrastructure-as-code, advanced automation, CI/CD
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datasets. Experience with GPU-accelerated computing or high-performance computing environments. Experience contributing to research proposals, supervising students or working within international
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 14 days ago
management, or storage integration * Databases, data platforms, distributed systems, GPU-enabled computing, or MLOps workflows * Experience providing technical leadership, mentoring developers, or owning major
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resources. Working knowledge of NVIDIA-specific technologies relevant to research computing, such as GPU-enabled workflows, CUDA-enabled tools, GPU resource use, or AI/ML computing environments. Basic
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documentation of HPC architectures, configurations, and operational procedures. Guide the architecture of the next-generation of GPUs through an intuitive and comprehensive grasp of how GPU architecture affects
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offer A Master’s thesis opportunity (6-12 months, or longer) at the intersection of AI and urban physics. Access to high-performance workstations and GPU resources for training large-scale models
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modules, AWS, Azure, GCP, or comparable services. Experience supporting GPU, AI/ML, CPU-intensive, statistical, imaging, compiler/toolchain, Fortran, MATLAB, R, Python, CUDA, or other discipline-specific
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methods on accelerated or heterogeneous computing platforms, including GPUs, FPGAs or other specialised hardware, for real-time or large-scale physics applications. We welcome candidates whose work is