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to apply machine learning techniques to a combination of experimental data and simulation results, aiming for faster and more accurate predictions. About us You will join an international and highly
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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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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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monitoring will be based on real time data streaming from the machine numerical control. The project will cover all the aspects related to the implementation and automation of the tool life cycle management
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monitoring will be based on real time data streaming from the machine numerical control. The project will cover all the aspects related to the implementation and automation of the tool life cycle management
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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