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Field
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computing frameworks (e.g., MPI, NCCL) and model parallelism techniques. Proficiency in C++/CUDA programming for GPU acceleration. Experience in optimizing deep learning models for inference (e.g., using
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research. Strong technical expertise in large scale data analysis, statistical and simulation tools, machine learning, AI and GPU programming. Experience with one more of these scientific collaborations
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performance. The Project/Program Manager (Systems Engineer) focuses on the deployment, configuration, monitoring, and maintenance of GPU-enabled servers, virtualized environments, and hybrid infrastructure
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partner with ORNL research organizations to enable research excellence and delivery. We work with other clustered computing and HPC groups to help research programs identify the best solutions
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administration. Experience with one or more of the following technologies: Automation User/Group Management Storage Systems GPU programming and GPU support Networking Preferred Qualifications: High Performance
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Description Primary Duties & Responsibilities: Lead the optimization of large-scale LLMs and deep learning architectures for biomedical research. Design and deploy high-performance AI systems using GPUs and
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support to meet individual user and program needs. The individual will independently collaborate with the IT partners on Campus and School of Medicine to fulfill the technical mission of the NCIBT
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engineering, and IT training and technical support to meet individual user and program needs. The individual will independently collaborate with the IT partners on Campus and School of Medicine to fulfill
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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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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