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-doctoral Associate to work on a fascinating project focused on the development machine-learning powered digital twin system for the structural performance of civil engineering structures. The project is a
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the production of the CBC catalog. The successful candidate is expected to have strong analytical skills and experience with signal processing, Bayesian statistics and machine learning. Exemplary
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, deep reinforcement learning, and human-machine interaction. Facilitate strategic collaborations across departments within the College of Engineering and with the UM Miller School of Medicine, to advance
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the EPSRC progarmme grant on Quantum advnatage in Quantiative Quantum Simulation (QQQS), will involve adapting and using our existing ultracold erbium and potassium machine for studies of e.g. turbulence
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and associated environmental impacts. Contribute to short-term (2026 to 2030) and long-term (2030 to 2050) verticalisation forecasting models based on machine learning, and to their validation against
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National Energy Technology Laboratory (NETL) | Morgantown, West Virginia | United States | about 2 months ago
permeability of fluid flow through hydrate-bearing sediments, • High resolution visualizations of hydrate distributions and fluid migration in porous media under in situ conditions, and • Machine learning
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National Energy Technology Laboratory (NETL) | Morgantown, West Virginia | United States | 26 days ago
The ideal candidate would have some, but not necessarily all, knowledge of and skills in: Familiarity with artificial intelligence, machine learning, physics-informed neural networks, or reduced-order
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National Energy Technology Laboratory (NETL) | Morgantown, West Virginia | United States | about 14 hours ago
migration in porous media under in situ conditions, and • Machine learning application to gas hydrate system to develop efficient key parameter estimation tools and large-scale 3D geologic model for gas
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deep expertise in modern machine learning and a strong record of research accomplishment who are excited to advance foundation models, agentic systems, and new AI approaches for high-impact scientific
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needs. TEPP focuses on expanding experiential learning opportunities for students enrolled at Tribal College and Universities (TCUs) through hands-on activities and professional mentorship from industry