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, ranging from early devotionals and Bollywood mythologicals to art house and parallel cinema masterpieces to explore key themes in the history and religion of India with a focus on 20th century developments
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CRC2 & Mitchell Funded Tenure-Track Faculty Positions in Supercomtuping and High-Performance Computi
Queen's University - Electrical and Computer Engineering, Smith Engineering | Kingston Downtown, Ontario | Canada | about 9 hours agoshared memory node parallel programming models (e.g. OpenMP, CUDA/HIP, Kokkos, etc.) o the composition of communication libraries and node parallel programming (e.g. MPI+X). o I/O communication
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clusters, GPU-enabled systems, job schedulers (e.g., Slurm), and parallel computing workflows supporting simulations, bioinformatics, machine learning, or large-scale data analysis Experience managing
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://www.ucalgary.ca/indigenous/ii taapohtop Walking together in parallel paths, there is a respect and honour for our Indigenous guardians who have protected the lands and peoples and the wisdom that they bring, and
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to write scientific articles autonomously, evidenced by a first-author publication record. Communication and autonomy: Ability to work independently, manage several projects in parallel and communicate
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, ranging from early devotionals and Bollywood mythologicals to art house and parallel cinema masterpieces to explore key themes in the history and religion of India with a focus on 20th century developments
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practical knowledge of machine learning with large datasets Experience : Experience with cloud-based, parallel, or distributed computing environments Hands-on experience developing or deploying production ML
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practical knowledge of machine learning with large datasets Experience : Experience with cloud-based, parallel, or distributed computing environments Hands-on experience developing or deploying production ML
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knowledge of machine learning with large datasets Experience : Experience with cloud-based, parallel, or distributed computing environments Hands-on experience developing or deploying production ML/AI systems
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knowledge of machine learning with large datasets Experience : Experience with cloud-based, parallel, or distributed computing environments Hands-on experience developing or deploying production ML/AI systems