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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | about 1 month 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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about the ones you don’t check! Familiarity with the theory of electromagnetic fields and waves. Experience with programming in C++ and / or Python. Some exposure to parallel computing, e.g., OpenMP, MPI
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Ability to code in a programming language to solve computational problems Awareness of computational infrastructure and its upkeep Ability to work on multiple projects in parallel and set priorities
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the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The ANR SPECT-Motion
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Our institute (LBMC) is located on the Monod site
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) Established Researcher (R3) Country France Application Deadline 24 Sep 2025 - 22:00 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not
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Application Deadline 15 Sep 2025 - 23:59 (Europe/Lisbon) Type of Contract Not Applicable Job Status Not Applicable Is the job funded through the EU Research Framework Programme? Not funded by a EU programme
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; strong physical, mathematical, and computational background; experience with programming in at least one general-purpose language (preferably Julia) and parallel computing; demonstrated effective written
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current practice). Demonstrated expertise in AI/ML. Proven track record in application performance optimization. Advanced experience with parallel programming models (e.g., OpenMP, MPI, CUDA). Extensive
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/GPUs. These devices provide massive spatial parallelism and are well-suited for dataflow programming paradigms. However, optimizing and porting code efficiently to these architectures remains a key