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particular, we aim to develop a neural network architecture that will allow us to accelerate solving AC power flow (AC-PF) computations, potentially facilitating real‑time contingency analysis, rapid design
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efficient model parallel training Implementation and optimization of GPU-accelerated training pipelines Validation of models on patient-specific geometries obtained from MRI data Participation in conferences
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prediction Integration of domain decomposition methods into the learning framework to enable efficient model parallel training Implementation and optimization of GPU-accelerated training pipelines Validation
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to accelerate solving AC power flow (AC-PF) computations, potentially facilitating real‑time contingency analysis, rapid design‑space exploration, and on‑line operational optimization of power systems
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scientists, engineers, and experts are accelerating energy innovation through breakthrough research and systems integration. From our mission to our collaborative culture, NLR stands out in the research
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Laboratory of the Rockies (NLR), where world-class scientists, engineers, and experts are accelerating energy innovation through breakthrough research and systems integration. From our mission to our
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, and international collaboration opportunities. Learn more about Monash . Today, we have the momentum to create the future we need for generations to come. Accelerate your change here. Monash supports
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Kick-start your career as an undergraduate engineer in the energy industry by registering for our Amplify Program. Our Amplify Program is a targeted career accelerator that enables undergraduate
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About the role We wish to appoint a Research Assistant to undertake fundamental research on markets and regulation for energy networks to support accelerated energy transition. The successful
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of the original computations at a fraction of the cost. This hybridization aims not only to accelerate performance but also to maintain, if not improve, analytical rigor. The improved modules will be integrated