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science, conversational AI, and marketing analytics and on substantial investments in on-premises GPU infrastructure that allows us to work with confidential partner data under full data control. The focus
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infrastructure, including high-performance/GPU compute, research data storage, and collaboration with UT Austin's central research computing resources such as TACC, with clear separation between research and
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. · Knowledge in servers with GPUs, and development of Artificial Intelligence applications. · Solid understanding of Pattern Recognition, Machine Learning, and Deep Learning with applications to health issues
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resources, including Texas Advanced Computing Center (TACC), and multiple HPCs on campus, including some GPU-heavy clusters as well as the molecular biology, flow cytometry, and imaging equipment in the Life
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education or research environment 3+ years of relevant professional experience Experience in GPU computing Experience with Github for software management Experience with the usage of workflow languages (like
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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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for advanced research. A7. Knowledge of scaling and optimising software to take advantage of GPU / HPC infrastructure. Desirable: B1. Knowledge of Trusted Research Environments out with or within an HPC
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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