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fields, applying advanced techniques such as large-scale data processing and GPU-accelerated computing. Access to state-of-the-art research facilities and a new GPU cluster. Collaborative and inclusive
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, proteins, chemical structures, geospatial, oceanographic, or heath record data. Experience in CUDA GPU programming. Experience authoring open-source Python packages in PyPI. Familiarity with RESTful web
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be admitted to the PhD program in Science and Technology. The education includes relevant courses amounting to about six months of study, a dissertation based on independent research, participation in
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Science, or a related field Strong programming skills in Python, R Solid understanding of: Machine learning fundamentals Deep learning architecture (e.g., CNNs, RNNs, Transformers) Optimization and model
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1,000 full-time instructional faculty. JMU offers thriving programs in the liberal arts, science and technology, and professional disciplines at the undergraduate, master’s and doctoral levels. JMU has
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, mathematics or any related field. What we offer State of the art on-site high performance/GPU compute facilities Competitive research in an inspiring, world-class environment A wide range of offers to help you
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 15 hours ago
. The researcher(s) will be provided access to state-of-the-art supercomputing facilities with advanced GPU and data storage capabilities. Additionally, opportunities will be available for collaborations. Duties
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commonly used on Unix systems. Additional languages or experience with libraries for utilizing GPU hardware efficiently, e.g., CUDA, are a plus. Experience in AI programming with, e.g., PyTorch(-DDP
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part of the core PLI team, which includes top-tier faculty, research fellows, scientists, software engineers, postdocs, and graduate students. Fellows will have access to the AI Lab GPU cluster (300
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approaches, the application of meta learning, and the integration of convex optimization layers Increase inference efficiency (e.g., GPU acceleration) and assess the applicability domain of learned algorithms