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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 16 days ago
. Description: This opportunity supports NASA’s Heliophysics strategic priorities by advancing understanding of Sun–planet interactions and enabling improved space weather prediction and exploration preparedness
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, observations, a hierarchy of numerical models, and machine-learning methods to understand their formation, dynamics, and predictability. The successful candidate will have substantial freedom to develop
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, Biostatistics, Public Health, Population Health Sciences, Health Services Research, Data Science, Cancer Prevention/Control, Quantitative Social Science, or a closely related field. Strong understanding
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system models and quantifying uncertainty in their predictions. Conduct fundamental research on the formulation of probabilistic system dynamics models, including knowledge integration, likelihood
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-regulatory elements and transcription factor genes that control key physiological pathways to specifically adjust targeted gene expression to reshape complex traits. The project will develop a breakthrough
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emulation, active-comparator new-user designs, self-controlled case series and case-crossover studies, where appropriate. The role will also involve developing and validating clinical risk prediction models
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analyze spatial and temporal datasets collected from UAS, satellite, ground-based sensors, and other sources. Integrate multi-source datasets and develop predictive models to support crop monitoring and
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Position Title: Postdoctoral Research Associate in Plant Breeding and Genomic Prediction Appointment Type: Post Doc/Trainee Job Description: Summary of Duties and Responsibilities: We are seeking a
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, such as multi-objective optimization, model predictive control, mixed-integer optimization, stochastic optimization, energy management, or production scheduling. Good knowledge of integrated energy
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generation of MOF water adsorbents with optimal indoor air humidity control performance by leveraging state-of- the-art high-throughput (HT) computational screening based on Machine-Learning Interatomic