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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 5 days ago
- Parallel Programming (emergency posting) Course description: Introduction to aspects of parallel programming. Topics include computer instruction execution, instruction-level parallelism, memory system
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parallel processing, FPGA coding and analysis, along with Machine Learning and AI based image analysis. The final aim of the project will be to generate in-situ / live film profile data to coating line
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Serve as the Lead for the team ensuring smooth operation of the Linux cluster consisting of 300+ GPU/CPU compute nodes including parallel filesystems and high-performance network. This is partly
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permeability selection in variant membrane chemistries. This work will run in parallel to experimental analysis conducted at the University of Exeter using synthetic vesicles to observe permeability
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times through higher parallelization and enable targeted stimulation of hardware faults by adjusting the models. To this end, a simulation environment based on a virtual prototype will be developed using
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laboratory and database administration. Experience with processing data from massively parallel DNA sequencers, computation on high performance compute clusters, using open source database management systems
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project management skills, and the ability to prioritise work, work to deadlines and undertake multiple parallel projects Demonstrated ability to identify, assess, test and evaluate technical applications
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of custom computer code, and the processing of large-scale simulations using massively parallel resources such as NERSC and Raj cluster at Marquette. Conduct research with a significant degree of independence
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optimize large-scale distributed training frameworks (e.g., data parallelism, tensor parallelism, pipeline parallelism). Develop high-performance inference engines, improving latency, throughput, and memory
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, inclusion, and accessibility by fostering a respectful workplace – in how we treat one another, work together, and measure success. As a U.S. Department of Energy (DOE) Office of Science national laboratory