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geospatial modeling approaches to analyze urban environmental processes. • Apply machine learning and geospatial data analysis techniques to large Earth Observation datasets. • Produce high-quality
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to environmental, nutritional, and economic analyses, support the development of new modelling approaches and datasets, and collaborate on high-impact research outputs and publications. You must hold a PhD/DPhil in
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, epidemiology, veterinary population medicine, quantitative biology, environmental science, or a related field Demonstrated experience developing statistical or machine-learning models using R or similar
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to eCO2, contributing to climate models and projections. The fellow will be based at the State University of Campinas's Center for Environmental Studies and Research (NEPAM-UNICAMP) in São Paulo state
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hydrological modelling, time-series analysis, and environmental data analysis. • Demonstrated experience in artificial intelligence, including machine learning and deep learning, applied to hydrological
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, Environmental Science, Remote Sensing, or related field Experience in atmospheric modeling, satellite remote sensing, or machine learning Programming skills (Python or R) Strong publication record Where to apply
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Earth Observation data analysis and/or spatial modeling Proven ability to publish in high impact peer-reviewed international journals Experience with machine/deep learning / AI applied to environmental
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, training, and evaluating neural network models Experience applying machine learning to large geospatial, Earth observation, or environmental datasets Ability to work independently and take responsibility
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or mitochondrial dysfunction. Perform experiments utilizing molecular, genetic, cellular and imaging approaches in both rodent and cell models. Participate in the development and validation of rodent and cell models
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upon extraction and processing technologies that have reduced energy requirements and lower environmental impacts. Recent work has confirmed that LiVS are the result of the explosive eruption of Li-rich