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discovery, and computational design. Extending our existing theoretical frameworks in investigating design optimization and nonlinear parameter estimation. Contributing to the teaching and assisting with
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. Extending our existing theoretical frameworks in investigating design optimization and nonlinear parameter estimation. Contributing to the teaching and assisting with dynamics relevant courses
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parameters that have been learned from data. For instance, why does a machine learning model predict that it is unsafe to discharge a certain patient from the intensive care? Or which characteristics make a
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incomprehensible model parameters that have been learned from data. For instance, why does a machine learning model predict that it is unsafe to discharge a certain patient from the intensive care? Or which
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exploratory eye and body movements during art (exhibitions) experiences and relate those to parameters of lighting design and perception. This project will be supervised by Maarten Wijntjes and Sylvia Pont. PhD
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have control over our spatiotemporal sampling of observational data locations, we first define the population. Next, we sample population units for (1) design-based estimation of population parameters
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units for (1) design-based estimation of population parameters, such as the mean and standard deviation of some target property or for (2) model-based prediction (i.e., mapping) of environmental
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23 Aug 2025 Job Information Organisation/Company Leiden University Research Field Computer science » Informatics Computer science » Programming Engineering » Computer engineering Engineering
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learning models keep getting better, but how they make their decisions often remains unclear, because these depend on many incomprehensible model parameters that have been learned from data. For instance
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? Generative AI and large-language models (LLMs) are about to turn computer-aided engineering into true human–AI co-design. In the new MSCA Doctoral Network GenAIDE we team up with Honda Research Institute