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AI models. Identifying relevant modalities to enhance prediction performance, with a focus on multi-spectral sensors, will be a key research area. Additionally, anomaly detection for modalities other
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platforms can unify production environments, enabling predictive maintenance and data-driven optimization through centralized data platform architectures. Your research will focus on addressing current
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generation by developing ML-based dual stabilization techniques. These techniques aim to predict and control the behavior of dual variables, reducing oscillations and improving the efficiency of the iterative
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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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process. An integral part of the project will be the development of enhanced data-driven physics methods to achieve reliable prediction of material removal rate and material removal distribution
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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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the project will be the development of physics enhanced data driven methods to achieve reliable prediction of residual usable life of milling tools. The approach will be validated by application to industrial
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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