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Field
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software development with GPUs. Experience with visualization of large data sets. An understanding of how to make results easily available using common web interfaces. Required Documents Resume Cover Letter
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suite of software, and its deployment on the university HPC & GPU based system. The position is primarily research and enterprise, but there would be a contribution of up to 20% to teaching, including
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of ORNL’s AI/ML tools, leveraging high-performance computing resources and AI-focused GPUs. Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity
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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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be expected to utilise or support the development and enhancement of our fire modelling suite of software, and its deployment on the university HPC & GPU based system. The position is primarily
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. Computational Infrastructure: Deploy and maintain high-performance computing environments (GPU clusters, cloud services) for large-scale image-text experimentation. Data Engineering: Establish workflows
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on conventional computing platforms such as GPUs, CPUs and TPUs. As language models become essential tools in society, there is a critical need to optimize their inference for edge and embedded systems
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bioinformatic pipelines. The analyses will be carried out on GPUs and part will consist of data processing and visualization in order to facilitate interpretation and, in some instances, clinical reporting
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computing (HPC) systems, including CPU, GPU, storage, file systems, networking, visualization, job schedulers, and scientific applications Experience leading the implementation and execution of research
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(CNNs, vision transformers), multi-GPU multi-node training, and federated learning strongly encouraged. Experience working with cloud platforms. Experience with high performance computing. Publication