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Field
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. Duties: Develop and execute a strategic roadmap for research computing infrastructure, including GPU-enabled high-performance computing (HPC) environments and enterprise storage systems including
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like OpenCV. ● Cloud & HPC Infrastructure: Experience configuring and managing environments in AWS/Azure or High-Performance Computing clusters for GPU-intensive tasks. ● Generative Media: Hands
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managing environments in AWS/Azure or High-Performance Computing clusters for GPU-intensive tasks. Desired: Master’s degree in computer science, Data Science, Digital Humanities, or a related field
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configuring and managing environments in AWS/Azure or High-Performance Computing clusters for GPU-intensive tasks. Desired: Master’s degree in computer science, Data Science, Digital Humanities, or a related
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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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of cores, and a growing GPU cluster containing thousands of high-end GPUs. Depending on the day, we might be diving deep into market data, tuning hyperparameters, debugging distributed training performance
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hardware architects to establish how agentic AI and these languages co‑design with heterogeneous HPC systems (CPUs, GPUs, PIM, AI accelerators). Study performance and portability tradeoffs, leveraging
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. Qualifications: Familiarity with machine learning interatomic potentials, CPU and GPU parallelization, knowledge of LAMMPS and molecular dynamics, experience with first principles calculations of dielectric and
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agentic workflow (Llama, Claude, LangGraph, etc) Experience with training models on GPUs Experience working with Unix and/or Linux, including shell scripting Experience implementing NLP/AI algorithms and
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. The role involves the design, implementation, and testing of GPU compute kernels, and associated host code, for the CHR real-time pipeline. Particular challenges include high-throughput beamforming via a