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Field
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evaluation of converter and drive architectures for edge-resident intelligence; the development of advanced modulation, estimation and control algorithms for high-performance operation; and the
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solving security assessment and optimization problems at unprecedented speed and scale. While high-performance computing (HPC) has become the backbone of large-scale power system simulation, emerging
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trade-offs between predictive performance, computational efficiency and scalability, using high-performance and cloud computing environments. Depending on the agreed research direction, you may also
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operation and very low energy consumption. The overall aim is to establish new device concepts that could contribute to high-performance classical computing and emerging brain-inspired computing hardware
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open to work with researchers with varying academic backgrounds. Those with experience in, for example, machine learning, high-performance computing, reconfigurable hardware, cloud computing, compiler
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scientific computing, control theory, data science, data driven methods, discrete mathematics, discretization methods, graph algorithms, high-performance computing, integral equations and nonlocal models
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support team that operates as part of a national platform providing high performance computing resources for academic researchers from all disciplines and regions of Canada. Your opportunity: SciNet is
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developing research code in Python and working with large datasets; experience with C++, scientific computing, or high-performance computing is a plus * Excellent problem-solving skills and an interest
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technology non-idealites make aIMC scalability to large scale generative AI models with high performance challenging. Your work will span device-to-algorithm co-design, focusing on state-of-the-art small
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biology is not required good written and spoken English Experience with PDE-based simulations, finite element methods, high-performance computing or deep-learning frameworks (e.g. PyTorch, JAX) is an