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experiments. The objective is to develop Bayesian causal models and neural networks capable of identifying relevant causal relationships between instrumental parameters and observed anomalies. The work will
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strategies to mitigate impacts on adjacent waters. Research activities include: coordinating with multiple stakeholders and collaborators to define objectives and research questions; leading participatory co
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functional data ”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case
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a spatially explicit predictive model for Everglades vegetation dynamics in response to major drivers. The major objectives are to explore the distribution models that discriminate among prairie and
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include: coordinating with multiple stakeholders and collaborators to define objectives and research questions; leading participatory co-development and refinement of conceptual models; devising management