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contribute to developing and improving the performance of next-generation thermal systems—including HVAC, water heating, and data center cooling—at both the system and component level. This position resides in
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characteristics and system-level requirements, and their suitability for scientific applications. The candidate will develop modeling and simulation capabilities to quantitatively evaluate quantum, classical, and
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(NSSD). In this role, you will conduct fundamental research into the integration of Bayesian methodologies with system dynamics modeling, advancing statistical methods and the open-source scientific
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planning principles and frameworks that can be applied to various urban environments. Monitor and evaluate multi-energy-system integration solutions to ensure they comply with regulations and standards
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) to work on the Terrestrial Ecosystem Science Scientific Focus Area (TES SFA). The successful candidate will contribute to the development and evaluation of the Energy Exascale Earth System Model's land
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) to work on the Terrestrial Ecosystem Science Scientific Focus Area (TES SFA). The successful candidate will contribute to the development and evaluation of the Energy Exascale Earth System Model's land
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electronics wastes. In this effort, the candidate will have a direct impact in informing the U.S. Department of Energy’s approach to e-waste recycling. The second approach involves supporting manufacturers and
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the online application system or need an accommodation to apply due to a disability, please email: [email protected]. #LI-DC1 This position will remain open for a minimum of 5 days after which it will
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Employee Discounts. If you have difficulty using the online application system or need an accommodation to apply due to a disability, please email: [email protected] . #LI-DC1 This position will remain
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multidisciplinary team. Excellent interpersonal, oral, and written communication skills. Preferred Qualifications: Experience with AI/ML approaches for spatiotemporal or Earth system data, including generative AI