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Virtual Training Environment (VTE) for disaster response simulation, integration of Building Information Modelling (BIM) with Structural Health Monitoring (SHM) using smart sensor networks, and resilience
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. The position will involve development of codes/models simulating the nucleation and propagation of stress corrosion cracking in samples under low cycle fatigue conditions, as well as models for linking
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, synthetic biology, mathematical modelling and AI/ML and more to design the next generation microbial cell factories. We do this with “the end in mind,” meaning that we have a commercial and industrial
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mathematical foundation of machine learning models. You will be responsible for developing scientific machine learning methodologies enabling new approaches for solving machine learning problems including
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-cutting and bending to break the glass panels. The project will involve the establishment of a numerical model and the acquisition and analysis of data from physical measurements in the production
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and kinetic modelling Expression, purification, and characterization of enzymes from fungal and bacterial sources Development and optimization of enzyme assays Structure–function studies of enzymes
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characterization of glycoside hydrolases, and a postdoc working on computational modelling of the same enzymes. The PhD focuses on ligand-observed NMR analyses and other relevant methods to provide insight
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research focus will include some of the following topics: Advanced sensor fusion and multimodal AI models for robotic intercropping. Self-supervised learning will generate multimodal agricultural pre-trained
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Madsen in an international environment at DTU Chemistry. We are interested in the discovery and characterization of novel chemical reactions proceeding in water at mild conditions and with environmentally
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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