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Field
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optimization layers Increase inference efficiency (e.g., GPU acceleration) and assess the applicability domain of learned algorithms Publish and present your results in peer-reviewed journals and at
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-atmosphere dynamics. We will build an AI-enabled modeling system that couples a GPU-optimized ocean model with a biogeochemical module and AI-based, kilometer-scale atmospheric forecasts. This system will
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their publications Experience programming GPUs with CUDA, SYCL, HIP or OpenMP Experience using and developing code with AMReX Experience in performance engineering to improve code scalability and reduce time-to
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intelligence models (LLMs) in multi-GPU environments. Preparation of technical documentation, best practices for development and operation. Where to apply Website https://sede.uvigo.gal/public/catalog-detail
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of computer graphics fundamentals, numerical methods, and GPU/parallel computing concepts. Experience with at least one major deep learning framework (PyTorch preferred). Excellent problem-solving skills and
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networking technologies such as InfiniBand. Working knowledge of GPU technologies like CUDA and OpenCL. Experience with distributed computing job schedulers (e.g., Slurm, PBS). Familiarity with
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Position Summary: The Research Engineer will be responsible for the smooth operation of the VIDAR Lab hardware and software stacks, including GPU clusters and related computing resources. This position will
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Engineers. Serve as liaison with Princeton Research Computing staff on GPU cluster related issues. Professional Development Learn the underlying science, mathematics, statistics, data analysis, and algorithms
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platforms such as llm servers, shared virtual GPUs (VGPUs) used by OPS-G, and the broader utilization of cloud resources. Ensuring the smooth operation, availability, and continuous improvement
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infrastructure, and GPU/CPU cluster environments. This role leads and mentors a team of Systems Engineers and Administrators while remaining deeply technical and hands-on, actively designing, deploying, and tuning