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Field
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, Ansible, or similar technologies. Experience with Git-based development workflows, CI/CD, infrastructure-as-code, or modern DevOps practices. Experience designing infrastructure supporting HPC, AI/ML, GPU
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. This position will work within Research Computing to architect advanced computing, GPU-enabled environments and serve as a specialized partner to researchers to develop AI/ML research workflows within UAB
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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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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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distributed training concepts, GPU-based training, checkpointing, experiment tracking, training stability and compute optimisation. MLOps, infrastructure and deployment: Familiarity with Linux environments
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practices and international standards. Leverage VIB Data Core services, including high-performance computing pipelines and large-scale GPU resources, to scale ML development and deployment. Your profile PhD
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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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. · 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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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