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, energy, and critical materials supply chains. In this role, you will be responsible for analyzing existing and future supply chains, with a specific focus on understanding the complexities and challenges
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facilities in partnership with the computational science community. We help researchers solve some of the world’s largest and most complex problems with our unique combination of supercomputing resources and
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cases. This position is focused on advancing the capabilities of LLMs to address complex problems within specific scientific domains, with an emphasis on climate risk assessment and analysis. As part of a
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
Requirements Required skills, abilities, and knowledge: Recent or soon-to-be completed PhD (within the last 0-5 years) by the start of the appointment in computer science, electrical engineering, applied
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facilities in partnership with the computational science community. We help researchers solve some of the world’s largest and most complex problems with our unique combination of supercomputing resources and
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future grid challenges, including widespread integration of DERs. The selected candidate will model, analyze, and control electric distribution and transmission systems, applying state-of-the-art
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simulation laboratory as well as supporting ongoing DOE projects. Candidates will be required to work in at least 4 of the following areas: Develop a functioning test bed using Opal-RT PHIL real-time platform
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of complex propulsion systems involving modeling of multi-phase flows, turbulent combustion, heat transfer, combustion, and thermo-mechanical fluid-structure interaction by further developing commercial/in
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machine learning expertise, with the goal to enhance predictive capability and scalability of multi-scale and multi-physics simulation codes. Perform high-fidelity CFD simulations of complex physical
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-driven initiatives within computational science Effective communication skills, both verbal and written, for effective collaboration with interdisciplinary teams and clear presentation of complex technical