2,719 parallel-and-distributed-computing-phd-"Meta"-"Meta" Postdoctoral positions in United States
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and conference presentations Active participation in professional organizations Basic Qualifications: A PhD in Mathematics, Applied Mathematics, Computational Science, or a related field completed
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in professional organizations Basic Qualifications: A PhD in Mathematics, Applied Mathematics, Computational Science, or a related field completed within the last 5 years Preferred Qualifications
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Location: Cambridge, Massachusetts 02139, United States of America [map ] Subject Areas: Physics / Quantum Information , Quantum Information Science , quantum information theory , quantum information
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curious to deliver work that matters, your journey starts here! The Department of Electrical and Computer Engineering (ECE) ranks among the best in the country. Our research programs are at the forefront
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/2312.14804v2 Qualifications Basic qualifications (required at time of application) PhD or equivalent international degree, or enrolled in a PhD or equivalent international degree granting program Additional
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programming; Experience programming distributed systems; Experience with parallel and distributed File Systems (e.g., Lustre, GPFS, Ceph) development. Advanced experience with high-performance computing and/or
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opportunities for a broader range of science-based applications What will I be doing? As a postdoctoral fellow in the FES Postdoctoral Research Program, you will conduct your proposed research related to the FES
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 12 days ago
and Experience: Distributed parallel training and parameter-efficient tuning. Familiarity with multi-modal foundation models, HITL techniques, and prompt engineering. Experience with LLM fine-tuning
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. Washington Monthly Magazine ranks FIU among the top 20 public universities contributing to the public good. The Department of Electrical and Computer Engineering at Florida International University is seeking
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computed tomography (CT) reconstruction, including sparse-view and limited-angle algorithms, and the application of advanced machine learning (ML) and computational imaging methods to scientific and