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using methods such as Dynamic Mode Decomposition with control (DMDc). You will also assist in the development of predictive control approaches based on reduced-order models, and contribute to workflow
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interaction scores. Build and deploy machine learning and statistical models for functional genomics predictions, including sgRNA efficiency and drug sensitivity scoring. Collaborate with laboratory members
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as predictors of plant function and community assembly --- into predictive computer models of terrestrial ecosystems, land-atmosphere interactions, and the Earth System. Field of Science: Earth Science
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statistical modeling, machine learning, data analysis, and reporting Proficiency in Python or R Ability to plan, execute and control a project, establishing realistic estimates and reporting timelines Advanced
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(HAC). This role focuses on applying advanced computational and analytical methods—including artificial intelligence, machine learning, deep learning, time-series modeling, and large language models
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computational modeling for astronaut risk prediction; & interact with recognized university and industry collaborators. Field of Science: Biological Sciences Advisors: Joshua Alwood Joshua.s.alwood@nasa.gov (650
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they are Legal Permanent Residents of the United States. A complete list of Designated Countries can be found at: https://www.nasa.gov/oiir/export-control . Eligibility is currently open to: U.S. Citizens; U.S
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, unless they are Legal Permanent Residents of the United States. A complete list of Designated Countries can be found at: https://www.nasa.gov/oiir/export-control . Eligibility is currently open to: U.S
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Are you a researcher driven to understand and predict the fundamental mechanisms limiting lithium-ion battery performance? We are recruiting a Research Associate in Lithium-Ion Battery Modelling
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and measurement noise. • Developing predictive control strategies based on reinforcement learning or hybrid approaches compatible with real-time adaptive optics constraints. • Proposing and