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-wide traffic prediction Design physically consistent and interpretable machine-learning methods for dynamic traffic systems Test and validate prediction models using large-scale real-world traffic data
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validate prediction models using large-scale real-world traffic data from Dutch freeway networks. PhD Position 2 - Data Assimilation and Network State Estimation This PhD focuses on estimating key traffic
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phenotyping, microbiome profiling, predictive modelling and targeted experimental validation to identify the microbial factors that make plants more resilient to disease. Your job You will join the Plant
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technologies based on a digital twin. The digital twin will combine information from the physical structure with models and monitoring data to assess its current structural state and predict its remaining
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plant phenotyping, microbiome profiling, predictive modelling and targeted experimental validation to identify the microbial factors that make plants more resilient to disease. Your job You will join the
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and numerical models that predict material deformation, force transmission to cells and the resulting changes in cellular mechanics and signalling. The position is primarily computational, but a
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models and monitoring data to assess its current structural state and predict its remaining lifetime. This will enable the condition of welded structures to be monitored throughout their service life
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expand and improve an existing modelling framework to predict direct and indirect nitrous oxide and methane emissions from agriculture. You contribute to the following activities: Performing a SWOT
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. To address this challenge, the project aims to develop structural health monitoring technologies based on a digital twin. The digital twin will combine information from the physical structure with models and
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in order to recruit 11 outstanding PhD students. The MoM section is recognized worldwide for its high-level research on experimental analysis, theoretical understanding and predictive modelling