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Field
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-driven surrogate models for real-time reconstruction and forward simulations. Create numerical algorithms for physics reconstruction using sparse data. Implement assimilation pipelines which integrate
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, converter control, and energy systems. Design, model, simulate, and develop advanced power electronic converter topologies and associated control algorithms. Investigate high-efficiency and high-power-density
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implications for climate risk assessment and societal resilience. The team uses integrated observations, reanalysis datasets, climate model simulations, artificial intelligence, and statistical approaches across
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(Lua/Java), agent behavior modeling, event handling, and API-based integration with external AI systems. Experience with distributed systems, reinforcement learning, or simulation environments (e.g
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science, machine learning, AI, or any computational science discipline of interest to the Computing Sciences Area and Berkeley Lab. Fellows apply advances in these fields to computational modeling
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modelling and advanced cooling at high temperature of SiC devices. (6) Simulation model of new SiC devices (7) Loss modelling and analysis (8) EMI filter design (9) Gate driver design (high frequency and WBG
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simulation, and the integration of market design with power system planning and operation. Through analytical modelling, optimisation, simulation, and data-driven methods, the positions will contribute
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Research Council (ERC) Starting Grant. About the project/work tasks: How can we simulate the properties and dangers of extreme rainfall under conditions we are yet to observe, in a trustworthy way? RAPTURE
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network from high-impact, low-probability crisis events. This project addresses critical challenges in dynamic modelling, DT federation, and decision support to help stakeholders manage crises ranging from
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, and modeling tools (e.g., Python, MATLAB, TCAD, photonic simulation software). Strong analytical, problem-solving, communication, and teamwork skills, with the ability to work independently in a