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understood. Most current assessments are based on inflow–outflow measurements, providing limited insight into what happens inside the systems and leading to substantial uncertainty in design, modelling, and
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. Incorporating information about plasticity can aid genomic prediction modeling of tree growth and health under future climates. This project seeks to address generalizable principles underlying the genetic basis
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. The research in the PhD project will focus on core spatio-temporal machine learning method development, including: generative models for grid-based and particle-based spatio-temporal data; controlled generation
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evolve and adapt to new environments—both at the population level and within individual hosts. In addition, various experimental model systems are used to study in detail how virus-host interactions
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-resistant posture and physiology. The project aims to reconstruct, measure and predict the sensitivity and resilience of Swedish conifer forests to interacting soil fertility and droughts. Specifically, we