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methodologies and tools for economic and ecological analyses of hydropower systems. The position will involve the development and use of computer models, simulations, algorithms, databases, economic models, and
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modeling of crystals, dislocation dynamics, and defect analysis, linking atomic-scale simulations to macroscopic properties. Familiarity or interest in machine learning methods and computing frameworks
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experiments. Develop reinforcement learning models to improve gate fidelity. Leverage CNM’s state-of-the-art facilities, including the nanofabrication cleanroom and the Quantum Matter and Device Lab’s dilution
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management through status updates, technical research reports, project presentations, and other regular channels. Develop technical ideas and proposals to advance the understanding of molten salt
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of photosensitizers and solar fuels catalysts to be interrogated in-situ, under operando conditions and with atomic-scale resolution. Position Requirements The successful candidate will be highly motivated and have a
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in spatial analysis and data visualization Computer programming skills relevant for data manipulation and analysis Experience with creating and using complex data-driven analytical models using R
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reports for sponsors, and attend and make presentations at scientific meetings Communicate effectively with supervisors, peers, and Laboratory management through status updates, technical research reports
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reports for sponsors, and attend and make presentations at scientific meetings Communicate effectively with supervisors, peers, and Laboratory management through status updates, technical research reports
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will facilitate the comprehensive characterization of microelectronics under various conditions, including thermal, mechanical, and radiation stresses. The software developed through this project will
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
: Expertise in rare event simulation, deep learning, and developing computationally efficient approaches for simulation and modeling in complex systems is highly desirable Experience with parallel computing