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/ External / View Profile U of T Employee Sign In More Filter Options: Campus and Job Category Loading... Clear Create Alert Apply now » Sessional Lecturer - CSC367H1S - Parallel Programming Date
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position comprises research and teaching at bachelor's, master's and doctoral level in computer science, primarily in operating systems, compiler construction, systems programming, and parallel and
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and cloud computing. Documented ability to teach operating systems. Documented ability to teach compiler construction. Knowledge of parallel programming. Experience of software development or cloud
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Inria, the French national research institute for the digital sciences | Talence, Aquitaine | France | 10 days ago
core of the project is the exploration of leveraging task-based parallel and distributed programming techniques in the DDF-pipeline software components with the aim to accelerate such processing on high
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. Enforce security best practices including node hardening, kernel patching, and compliance across all systems. Manage parallel file systems such as Lustre, GPFS, Weka, or Vast, including performance
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computation, communication and storage, optimizing GPU utilization, parallelization, inter-node communication and I/O performance on parallel file systems. You will develop solutions for monitoring
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across Linux environments, programming and scripting languages, job scheduling, scientific applications, optimized compilers, mathematical libraries and algorithms, and distributed and parallel file
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research in leading journals and conferences. Attract competitive research funding in Denmark and Europe. Teach and develop courses in parallel computer systems, parallel programming, and HPC, and supervise
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., Ganglia, Grafana, or similar). Experience with parallel programming frameworks (e.g., MPI, OpenMP, CUDA) and high-performance interconnects (e.g., InfiniBand). Preferred Qualifications: Familiarity
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. Experience with performance tuning and benchmarking tools for HPC environments (e.g., Ganglia, Grafana, or similar). Experience with parallel programming frameworks (e.g., MPI, OpenMP, CUDA) and high