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Field
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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
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) therapy on the biology of γδ T cells and how can we use this knowledge to help us predict the success of therapy and prevent the development of side-effects. Position 1 will focus on the cellular and
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for optimizing metals microstructures in-situ during the AM process as well as ex-situ during post-AM treatments and enable predictions of the microstructural evolution, and thus changes in properties, while AM
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measured data, apply necessary filtering and selection of data features to be stored. Couple the numerical model and the measured input data to establish a model that can predict the outcome in terms
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restraint conditions. A key goal is to develop both a sensor system and a prediction model for the short- and long-term deformation behaviour of concrete. These tools will be applied to full-scale structural
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“Quantitative predictions of protein – DNA interactions from high-throughput biophysical binding data”. Sequence specific binding and recognition between transcription factors and DNA control gene expression at
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models. This theoretical project will facilitate close collaboration with experimental groups and enable benchmarking of theoretical predictions. The PhD researcher will be part of the Correlated Quantum
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Computer-adaptive methods and multi-stage testing Application of machine learning in psychometrics Predictive modeling of educational data Methodological challenges in cohort comparisons Advanced meta
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energy; thereby minimising farming’s environmental impact. AI machine learning offers a new expedient method of developing control systems for tasks that would be difficult to manage using classical
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will also include evaluating and validating existing numerical models, ensuring their reliability in predicting real-world conditions. This project is supported by brand-new laboratory facilities