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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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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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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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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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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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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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: 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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, 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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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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that can then be tested quickly in the lab rather than remain computational predictions? Do you also wish to work closely with experimental biologists and gain a solid grasp of how experimental work is