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parallel, the P.I. of the Sea Around Us has pursued research focused on the relationship between breathing by fish and aquatic invertebrates and the temperature of the water they live in, which will allow
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programming. Topics include computer instruction execution, instruction-level parallelism, memory system performance, task and data parallelism, parallel models (shared memory, message passing), synchronization
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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 | 3 days agoPosted: September 15, 2026 Tenure-Track/Tenured Faculty Positions in Supercomputing/ High-Performance Computing Department of Electrical and Computer Engineering Stephen J.R. Smith Faculty
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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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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The Werklund School of Education at the University of Calgary invites
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and local, national and international collaborators. Required qualifications Education: PhD in computer science, engineering, mathematics/statistics, computational neuroscience or a related quantitative
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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