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Field
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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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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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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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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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, advancing bioinformatics-driven research, facilitated by exceptional computational infrastructure including a centrally administered high-performance CPU and GPU cluster and network storage. This includes
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support the School’s teaching, research, and operational goals, including promoting innovative technologies in areas like robotics, AI, IoT, cybersecurity, and immersive technologies. Plan, implement, and
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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 for their needs
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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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sitting within a leading programme of clinical, health and bioinformatics at the South London and Maudsley (SLaM) Biomedical Research Centre (BRC) and forms a key component of both the Centre
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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