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Challenge: Solve computational bottlenecks in the modelling of mechanics of metallic systems. Change: Develop new physics-informed machine learning algorithms and predictive models. Impact: Enable
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researcher, you will help us design and run research line involving collaboration with PhD and MSc students. You will develop novel data-driven and physics-based modeling techniques, conduct and analyze
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Contribute to pioneering robotics research in model-based control of deformable mechanical systems by blending advanced theory with solid experimental validations. We seek a postdoctoral researcher
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Large Language Models for Knowledge Harvesting, Sharing, and Management Knowledge is a vital asset for individuals, organizations, institutions, and society. Large Language Models (LLMs) have the
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optimal inspection and maintenance strategies can be devised using geo-data, engineering models, and AI. The case study will be the city of Amsterdam, however, the methods to be developed will be generic
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model the spatial variability and distribution of mining residual piles in multiple dimensions. The research work entails collecting samples from test case sites, utilizing secondary data, and conducting
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may fail to capture the intricacies of the human body's response to motion. Advanced active human body models offer highly detailed information but require specialized expertise and significant
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of this PhD project are to develop and validate 1) algorithms to retrieve virtual reflection responses from multi-source-receiver GNSS-R data, and 2) full-wave inversion by joint model- and data-driven methods
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of the flow. This project aims to quantify this variability and relate it to variability in pollutant levels. New types of urban flow modelling, so-called large-eddy simulations, in combination with
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Human memories do not just capture a stream of data. What is important to memorise is determined by the way we conceptualize the world around us, by common cultural-cognitive perceptions, and by