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that can then be tested quickly in the lab rather than remain computational predictions? Do you also wish to work closely with experimental biologists and gain a solid grasp of how experimental work is
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, or predictive modeling—based on real experimental data. You will work closely with engineers, technicians, and the postdoc to build and refine data pipelines and interfaces. As part of your research training, you
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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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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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predictive maintenance models combining physical and ML approaches. Test, validate, and integrate developed solutions in real industrial environments. You must have a two-year master's degree (120 ECTS points
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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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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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) 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