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into production in a cloud environment Minimum three years’ experience using PyTorch, Tensorflow, or MXNet, along with optimizing code for GPU clusters Experience building advanced workflows such as retrieval
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modules, and monitor training progress. Display performance metrics (e.g., inference time, GPU utilization, throughput, ROI impact) in real time. System Integration Work with the research team to connect AI
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). Experience with distributed systems, GPU computing, or cloud-based simulation environments. Knowledge of human-in-the-loop simulation, training effectiveness evaluation, or synthetic environments. Experience
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, superconductivity, cryogenics, or microwave electronics. Additional experience beyond the PhD is not required. US citizenship is not required. What we offer State of the art on-site high performance/GPU compute
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UK biobank data and human imaging would be desirable. The applicant should have proven programming experience including Python and R as well as using HPC and GPU environments. The post offers
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a dedicated CPU+GPU computing cluster at the Massachusetts Green High Performance Computing Center. The appointment will be for a maximum of three years, with annual renewal contingent on performance
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background in machine learning, deep learning, and/or computer vision; Experience in programming. Python is a must, lower-level GPU programming experience is a bonus; Strong grasp on the English language
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, postdocs, and graduate students. Fellows will have access to the AI Lab GPU cluster (300 H100s). Ideal candidates will have a strong interest and proven experience in designing, understanding
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partners ML Systems & Infrastructure Design, build, and operate reproducible ML pipelines for training and inference (e.g., Snakemake or equivalent) including GPU/CPU scheduling, job queuing, and fault
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Postdoctoral Research Associate I (Computational Stellar Astrophysics) Posting Number req24359 Department Steward Observatory Department Website Link https://www.astro.arizona.edu Location Main Campus Address