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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | about 2 months ago
, modeling and data assimilation, and developing digital twin technologies. Candidates can expect to work with collaborative and dynamic environment that includes fundamental and applied research performed
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of process-based models and data assimilation techniques. Excellent skills in data visualization and communication. Preferred Experience in large-scale ecosystem modeling or GHG inventory analysis. Experience
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weather events such as floods, droughts, and heatwaves, integrating probabilistic approaches to account for uncertainties. Use data assimilation techniques to combine observational data with AI models
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methanotrophic bacteria to maximize carbon assimilation and conversion. We seek applicants with broad knowledge of molecular and synthetic biology tools and techniques. Minimum Qualifications Ph.D. in Microbiology
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-reviewed literature. Read literature articles, develop new ideas, and assimilate the information into his/her project design/interpretation. Dissemination of studies: Interact on a regular basis with their
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. The successful candidate will be able to: • assimilate a wide variety of information into assessments • contribute to the planning and execution of research and analysis
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other process modeling tools. https://agilebiofoundry.org/ https://bioesep.org/ The successful candidate will be able to: • assimilate a wide variety of information into assessments • contribute
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, implement, and evaluate computational models that assimilate 2-photon data (60%) Use a computer programming language to create novel neural network simulations (models) that include realistic simulations
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Detail Information Position Summary As a Postdoctoral Associate in the Department of Earth Sciences , this position will support an investigation entitled “Toward ice sheet surface data assimilation
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
to develop hybrid models for sea ice that combine coupled climate models and machine learning. Our previous work has demonstrated that neural networks can skillfully predict sea ice data assimilation