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Field
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and machine learning to establish a modeling framework that uses omic data for providing effective degradation rates of biomolecules and predictions of their impact on soil organic matter turnover
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resulting properties. However, two significant challenges persist in this domain. First, the extrapolation of ML predictions beyond the range of existing data remains problematic, as models often struggle
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to mechanical forces. We work with leading international groups on modeling and also conduct simulations at DTU. Our overarching goal is to understand and predict the mechanical behavior of metals during plastic
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: Computational Modelling: Employing simulation tools (e.g., GEANT4, light transport) to explore novel metamaterial designs, predict performance, and optimise key parameters such as timing resolution, light yield
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are expected to be significant in: Earth system science – by improving models of Earth surface evolution and enabling better predictions of landscape response to climate change. Engineering and applied physics
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domains. The scientific outcomes are expected to be significant in: Earth system science – by improving models of Earth surface evolution and enabling better predictions of landscape response to climate
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recovery in critical applications, including aerospace, healthcare, and industrial automation. Research Focus Areas: Predictive Analytics for Fault Detection: Develop AI models that predict potential system
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with microstructural features and failure mechanisms Development of models to describe degradation mechanisms and predict component lifetime Presentation of research findings at project meetings
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, the CAPeX approach to finding new electrocatalytic materials for energy conversion reactions uses state-of-the-art machine learning techniques, but experimental feedback is needed to improve the models and
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load predictions for wind turbines, specifically the foundations, with the ultimate objective of including structural health information in windfarm asset management to optimise structural lifetime