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, and interacting with pilots and passengers. Operates and becomes familiar with ground support equipment, such as the aircraft tug, ground power unit (GPU), lavatory service cart, de-icing cart, forklift
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computing (HPC) systems, including GPUs, and programming, such as using CUDA, MPI, AI/ML/DL, and advanced debuggers and performance analyzers. Familiarity with working on open-source projects. About UF
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. Desirable criteria Experience working with generative models or large language models Experience with GPU-based model training or cloud computing Knowledge of synthetic biology or regulatory sequence design
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, implementing efficient monitoring of the various deployments using Grafana and Prometheus and the autoscaling of compute nodes for CPU and GPU workloads across various cloud providers. Key responsibilities will
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to benefit from latest advances in hardware technologies the ICON model was ported to run on Graphics Processing Units (GPUs) and is one of the first model that can be used in production
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, GPUs, AI accelerators etc.) require high power demands with optimized power distribution networks (PDNs) to improve power efficiency and preserve power integrity. Integrated voltage regulators (IVRs
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managing experiments using GPUs Ability to visualize experimental results and learning curves Effective inter-personal and team-building skills Self-motivated with an ability to work independently and in a
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) Experience in maintaining high-quality code on Github Experience in running and managing experiments using GPUs Ability to visualize experimental results and learning curves Effective inter-personal and team
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into high-throughput GPU/HPC workflows, contributing to data management, metadata structuring, and semantic annotation Collaborate with experimentalists and theorists to validate extracted knowledge via in
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results. Machine Learning skills to automise comparison process. Unbiased approach to different theoretical models. Experience in HPC system usage and parallel/distributed computing. Knowledge in GPU-based