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Are you interested in advanced computational modelling and state-of-the-art scientific software? Join us in creating the Bayesian multiverse: a computational framework for robust statistical analyses
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technologies based on a digital twin. The digital twin will combine information from the physical structure with models and monitoring data to assess its current structural state and predict its remaining
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Develop advanced models to understand and predict piping and internal erosion in dikes. As a PhD researcher at TU Delft, you will connect fundamental fluid–soil interaction physics with
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: Experimental skills in the mechanical characterization of fiber reinforced composites or polymers; experience with fatigue testing is desired. Theoretical knowledge and skills in the modelling of (failure
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Develop advanced models to understand and predict piping
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. In this PhD project, you will investigate foundation models for automotive imaging radar. The goal is to learn general radar representations from largely unlabelled data that can generalize across
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biogeochemical processes that govern their behaviour. Job description The PhD candidate will develop and analyse new process-based mathematical models to improve our understanding and predictive capability
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scale radar datasets with high quality labels remain scarce. In this PhD project, you will investigate foundation models for automotive imaging radar. The goal is to learn general radar representations
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. To address this challenge, the project aims to develop structural health monitoring technologies based on a digital twin. The digital twin will combine information from the physical structure with models and
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children within authentic educational settings. The project combines experimental research, classroom studies and computational modelling to better understand what makes children curious, how they decide