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applications Multimodal data integration and prediction, including clinical, medical imaging, and sensor data (e.g. ECGs) The PhD student will have the opportunity to conduct both methodological and applied
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physical laws and multi-physics constraints into the GNN framework. This approach will enhance predictive accuracy, reduce computational costs, and facilitate the design, simulation, and optimization of high
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) to develop an accurate, stochastic multibody simulation model. This model dynamically updates key parameters to predict effective braking and acceleration capabilities, complementing existing organizational
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precision and minimal interruptions throughout a life cycle. Incorporating predictive models and advanced control using data opens up exciting new possibilities in this domain. The research activities within
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magnetoresistance and polaronic transport. This project aims to develop novel computational frameworks to understand and predict the structure of such materials. Job description You will develop computational methods
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part, it is essential to optimize many material factors and printing parameters. The project investigates aspects like nozzle design, closed-loop feedback, optimal toolpath prediction and computational