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improve clothing design or indoor air conditioning strategies. Currently, we rely at Empa on the use of thermal manikins that have multiple sensors installed to predict heat stress in several environments
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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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, stable isotope analyses, and predictive modeling. The PhD candidate will focus on assessing plant reliance on deep soil water and will work closely with a multidisciplinary team across the Swiss Federal
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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