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Field
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quantitative predictions testable against empirical data from diverse ecological contexts. We use methods from theoretical evolutionary biology, including optimal control theory, life history modelling, adaptive
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neuroimaging data constrained by patient's structural connectivity and tractography • Using the results of the TVB model fits to stratify patients and predict disease progression • Organizing and unifying
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fields). Strong quantitative skills and demonstrated expertise in predictive modeling and advanced computational methods (e.g., Multilevel Vector Autoregressive Models, Dynamic Structural Equation
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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | 36 minutes ago
control, and coronagraph system modeling. Location: Ames Research Center Moffet Field, California Field of Science:Planetary Science Advisors: Natasha Batalha natasha.e.batalha@nasa.gov 650-604-2813 Ruslan
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environment. Development of models to diagnose and predict battery performance and ageing. Participation in national and international research projects related with energy storage and its integration in
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predict the location of resources more accurately, it is necessary to model these processes jointly at the basin scale. However, directly solving geochemical equations is computationally expensive and
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product models and conditions.While main innovations are expected in control and simulation, empirical validation and demonstration are considered equally important to ensure industrial adoption. Therefore
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the freshwater budget. It is essential to comprehend the various components of sea level change in order to enhance our capacity to predict and adapt to future sea level changes. It is expected that research
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technology. Development of cutting edge foundation models for protein design, small molecule property prediction, or protein function prediction Data generation and curation, including molecular simulation and
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development of new computational and mathematical models to quantify and predict infectious disease risk, particularly for identifying high risk individuals and groups. The PDRA will translate conceptual