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Field
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massively parallel computing systems, including GPU-accelerated platforms. Develop and maintain scientific software, primarily in Python , using modern practices for software development, testing
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needs. The NanoSIMS lab is specialized for studies of presolar grains and ancient planetary materials. ASIAA has a dedicated CPU cluster, several GPU servers, as well as access to the National High
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administration and HPC environments, together with strong technical expertise in cluster management, storage systems, networking, scripting, and infrastructure automation. Experience with GPU computing
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systems, including surrogate modelling, training data design or reduced-order methods Programming experience in C++ (including OpenFOAM solver customisation), Fortran, or GPU computing (CUDA, OpenCL
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discovery. The CUIMC Central IT (CUIMCIT) High-Performance Computing (HPC) team supports this mission through a rapidly evolving research computing environment featuring advanced GPU platforms, large-scale
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mathematics, computational science or a related field. Solid programming skills (e.g. C++ and Python); experience with GPU computing (CUDA), high-performance computing or machine learning is a plus. A
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datasets. Experience running compute-intensive machine learning experiments, including on high-performance or GPU computing clusters. Familiarity with AI tools and workflows (e.g., LLMs, AI-assisted coding
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with GPU computing, distributed training or inference, high-performance computing systems, cloud-native platforms, edge devices, Internet of Things (IoT) systems, or federated/distributed data
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these fforts. A successful candidate will collaborate with scientists in DIS, Environmental Sciences division and Argonne leadership Computing Facility to further develop the AI foundation models
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-performance computing (HPC) GPU acceleration, where appropriate Development of scalable numerical algorithms for systems containing large numbers of lasers and optical modes An important component of