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Field
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has embraced the “infrastructure as code” approach to systems automation. You’ll be working across a range of predominately Linux based systems, including HPC and GPU accelerated compute, large-scale
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models including scaling models across a large set of GPUs; building or optimizing LLMs to tackle new, complex tasks; developing new models of brain circuits and function; and learning software engineering
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implemented in the Fortran programming language, and it relies on the platform CUDA for parallelization of the computation over several GPUs’ cores, and has interfaces with Matlab and Python for ease of use
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or more GPUs; ability to work with pre-existing codebases and get a training run going Research interest in one or more of the following: Applied ML, Natural Language Processing, Computer Vision
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, computational algebra, logic and programming languages. The department is housed in the newly constructed Science & Innovation Center which boasts Data Center with High Performance GPU Cluster and state
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models, (d) experience in using high performance computing systems with multiple nodes and GPUs and (e) drought metrics. Familiarity with Texas water resources and management practices. Experience working
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well as access to the group dedicated computing cluster environment with H100, L40s, and A40 GPUs. This post is funded by the UKRI Future Leaders Fellowship, a flexible long-term public funding scheme
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Engineering, or a related field Strong experience in building and optimizing AI systems using PyTorch, TensorFlow, or JAX Practical knowledge of NVIDIA GPU programming (CUDA) and experience with inference
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transcriptomics. Innovative visualization tools and highly automated analytical pipelines powered by GPU technology. Mentorship from experienced scientists in data analysis and management, with an expertise in
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conditions. Implementing a multimodal approach for large-scale data analysis using CPU and GPU Solutions at the UM6P Data Center. Innovate and improve image analysis algorithms for plant trait quantification