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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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by investigating decomposition processes under controlled conditions to unravel the mechanisms of peat decomposition, identify the main actors and predict the interaction between these actors and their
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data in the context of multigenerational families. · (Co-)developing predictive models based on very large registry, medical, social/environmental, and molecular data, aimed at predicting health
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representation of real-world marine structures and their corrosion processes. By integrating comprehensive sensor measurements, experimental data on corrosion, and advanced predictive modelling (including physics
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are challenging to detect early with conventional single-sensor approaches. To ensure reliability and enable predictive maintenance, there is a pressing need for AI-supported, high-speed non-destructive monitoring
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corrosion and embrittlement in reactor environments, remains incomplete, and key mechanical properties are scarce. Without reliable experimental data, our ability to develop predictive models