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to begin September 1, 2025. We will consider strong candidates in any research area but will prioritize Distributed and Parallel Computing. A PhD in computer science or a related area is required
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accelerators Hardware–software co-design for AI and transformer-based models. Responsibilities Conduct cutting-edge research in parallel computing and AI accelerators Develop and lead externally funded projects
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for the leadership and management of the EPSRC programme grant: Advancing Parallel Mesh Generation and Geometry Representation to Enable Industrially Relevant, High-Fidelity Simulations (REMODEL). The REMODEL project
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Science or related disciplines from a reputable university. Good knowledge in parallel and distributed computing, and algorithm design, perfomrance analysis and optimization. Demonstrated capability to conduct
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Science. The research focus of our group is on parallel computing, supercomputing, and performance tuning and optimization of advanced applications. Our team currently consists of 10 scientific and 3 administrative
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critical responsibilities. Key duties include microscopic FISH analysis, karyotyping analysis utilizing computer systems, FISH wet lab procedures, slide hybridization, PET slide preparation, and proficiency
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of Computer Science. The research focus of our group is on parallel computing, supercomputing, and performance tuning and optimization of advanced applications. Our team currently consists of 10 scientific and 3
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 2 months 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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future position is in the Research Group Scientific Computing at the Faculty of Computer Science. The research focus of our group is on parallel computing, supercomputing, and performance tuning and
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willingness to learn: High-performance computing (distributed systems, profiling, performance optimization), Training large AI models (PyTorch/JAX/TensorFlow, parallelization, mixed precision), Data analysis