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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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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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NIST only participates in the February and August reviews. The fire modeling community is actively working to develop the tools needed to quantitatively predict material and product flammability
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(SHM), physics-based modeling, and data-driven analytics to enable predictive, performance-based decision-making and improve infrastructure safety, resilience, and lifecycle performance. The candidate is
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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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macro- scales at IJL, and to train machine learning models to predict the microstructure evolution at larger scales and longer times at SIMAP lab and Laboratoire Analyse et Modélisation pour la Biologie
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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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. Transforms complex institutional data into actionable insights that inform planning, budgeting, and strategic initiatives. Develops predictive and statistical models, prepares analytical datasets using SQL and
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spectroscopy, especially applied to the analysis of lipids or oils. Experience in the application of chemometrics to develop predictive models Participation in competitive research projects related to the field
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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