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models that strengthen tractability, decomposition methods), learning-enhanced approximations (e.g., embedding-based representations of complex interactions, passenger flow estimation using generative
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learning emulators to represent forward operators for fire observations for atmospheric data assimilation Implement ensemble methods in the fire emissions estimation and estimate uncertainties Utilize
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emissions. Developing new statistical methods for the interpretation of atmospheric greenhouse gas data from satellites Building spatially explicit inventories of fluorinated greenhouse gas emissions
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 1 month ago
learning emulators to represent forward operators for fire observations for atmospheric data assimilation Implement ensemble methods in the fire emissions estimation and estimate uncertainties Utilize
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, oscillation, response-time, and evidence-accumulation phenomena. Implement simulation, parameter-estimation, and model-comparison methods in Python, MATLAB , R, or related computational environments
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motivated to develop AI methods and apply them to clinically or epidemiologically meaningful research questions. We are particularly interested in candidates with experience in longitudinal data
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ecology and evaluation of restoration projects to address the injury to marine mammal populations that occurred from the Deepwater Horizon oil spill. The incumbent will develop methods to use these data to
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methods of research, testing and data collection, analysis and evaluation, and writing reports which contain descriptive, analytical and evaluative content. The purpose of this role is to acquire the
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at the sponsor's facility. Responsibilities* - Evaluate and benchmark vision-language models for adaptive robotic perception in industrial applications - Develop robust perception methods for object
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, and related fields. Preferred Qualifications: Experience in optimization methods and/or efficiency analysis (DEA, stochastic frontier, dynamic optimization). Experience with production function