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Field
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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 and high-performance storage, and
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experience. PACU/Critical Care/GPU experience strongly preferred. Experience working with patients 6 weeks in age to 25 years of age preferred. Licensure/ Certifications: Current Massachusetts license as a
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. Experience with multiple deployment mechanisms like Diskless, Warewulf, and traditional deployment (cobbler, PXEboot, and/or Bright). Experience managing systems utilizing GPU (NVIDIA and AMD) clusters for AI
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the optimization of large-scale LLMs and deep learning architectures for biomedical research. Design and deploy high-performance AI systems using GPUs and hardware accelerators. Interact and collaborate
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implementing STIG processes and remediations Experience with GPU, SLURM and other HPC environments. Experience with CI/CD support and container administration. Deep familiarity with Red Hat Linux operating
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and a new GPU cluster. Opportunities for professional growth and career advancement. Collaborative and inclusive work environment that fosters creativity and innovation. Application of Domain Expertise
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, e.g. HPC cluster (CPUs and GPUs) or cloud. Proficiency with high performance computing and system architecture. Advanced skills and experience associated multiple of the following: artificial
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and GPUs) or cloud. Proficiency with high performance computing and system architecture. Advanced skills and experience associated multiple of the following: artificial intelligence; method and machine
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software stacks, including GPU clusters and related computing resources. This position will also play a key role in designing and implementing new features for research projects. The successful candidate
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free text of both biomedical literature and electronic patient records exploiting HPC, including GPUs embedded within NHS infrastructure. Development and deployment of ML operations software and tooling