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Field
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The Postdoctoral Researcher will join a new interdisciplinary project developing an AI-augmented decision science platform to understand and model high-stakes human judgments. This position will be jointly mentored
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The Postdoctoral Researcher will join a new interdisciplinary project developing an AI-augmented decision science platform to understand and model high-stakes human judgments. This position will be jointly mentored
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research positions in the field of atmospheric aerosol modeling. Positions may begin as soon as September 2025 . A Ph.D. in Mechanical and Aerospace Engineering, Civil and Environmental Engineering
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
modeling of large-scale dynamics in networks. This role involves creating large scale models of dynamic phenomena in electrical power networks and quantifying the risk of rare events to mitigate
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. The successful candidate should have expertise and experience in process modeling, techno-economic analysis (TEA) and life cycle analysis (LCA) of lithium-ion batteries and/or recycling and resources to products
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-atmosphere dynamics. We will build an AI-enabled modeling system that couples a GPU-optimized ocean model with a biogeochemical module and AI-based, kilometer-scale atmospheric forecasts. This system will
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- Exploring Foundational Models and Agentic AI to address challenges in energy storage and conversion. Position Requirements Candidates must meet the following qualifications: 1. Educational Background: - A
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systems for accelerating computational catalysis and experimental design. The successful candidate will contribute to building AI-native frameworks that combine first-principles modeling, machine-learning
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or more senior research positions to analyze and model the delivery of clean energy and industrial decarbonization infrastructure associated with net-zero transitions. The role will report to the Andlinger
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a particular emphasis on error-corrected methods for future fault-tolerant quantum computing. The algorithms will be designed to address key models of quantum materials, such as the Hubbard model