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Field
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. Implement scalable training and inference pipelines using modern ML tooling (e.g., PyTorch/TensorFlow/JAX), version control, containers, and HPC/GPU resources. Support the publication of intermediate data
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scalability of simulation workflows via: Parallelization and performance engineering GPU/accelerator optimization Algorithmic innovation Experience applying machine learning or AI to molecular simulation
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-efficient designs, GPUs and HPC), Data Science/AI/Machine Learning (e.g., fundamentals, trust and explainability, LLMs, autonomous systems, computer vision), Security (e.g., fundamentals, hardware/software
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education Familiarity with distributed computation solutions Familiarity with GPU profiling and optimization Experience with data/storage systems Strong prioritization, process, and time management skills
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programming LAMP stack design and implementation experience Knowledge of GPU and FPGA cluster management Experience with federal research compliance and security requirements Background in AI/ML computing
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-based HPC services, this role will involve supporting SAS researchers who use Penn’s new PARCC (Penn Advanced Research Computing Center) centralized HPC services, including both CPU and GPU cutting-edge
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hundreds of thousands of cores, and a growing GPU cluster containing thousands of high-end GPUs. We don’t believe in “one-size-fits-all” modeling solutions; we are open to and excited about applying all
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recently added the new AI NVIDIA GPU SuperPod (https://news.ufl.edu/2020/07/nvidia-partnership/ ) in support of the AI initiative, which aims to expand the role of AI in higher education and research (https
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plasma physics (XGC, IPPL). Expected qualifications: A Master's degree in Computer Science or Applied Mathematics. Necessary knowledge: Modern C++, GPU computing with CUDA/SYCL, MPI, Krylov solvers
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) and parallel computing, with a focus on cost-efficient and scalable model deployment. Skilled in working with medium-large scale multicore and heterogeneous (CPU + GPU) clusters. Excellent verbal and