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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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, 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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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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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
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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 2 hours 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