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Field
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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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for levitated systems — from gas sensing to probing physics beyond the Standard Model. You will join a diverse, motivated, and supportive team of academic staff and students in Delft. We foster an inspiring and
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! With your work, you will expand the family of quantum sensors and open new applications for levitated systems — from gas sensing to probing physics beyond the Standard Model. You will join a diverse
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on the mathematical theory enabling these advances, via asymptotic analysis and nonparametric modeling. You will work in the Statistics section at the Delft Institute of Applied Mathematics (DIAM
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needs to be supplied with rigorous uncertainty analysis. The focus of this project is on the mathematical theory enabling these advances, via asymptotic analysis and nonparametric modeling. You will work
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the development of a Virtual Training Environment (VTE) for disaster response simulation, integration of Building Information Modelling (BIM) with Structural Health Monitoring (SHM) using smart sensor networks, and
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metrics. If the data is used for mapping environmental properties, the fitted model performs poorly on minority classes or parts of the map cannot be trusted because they correspond to areas not covered by
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maintain robustness through evolution using live-cell imaging and multiscale modelling. Job description Cells are often described as intricate machines where proteins work together in a tightly coordinated
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at the heart of these crises: they determine how ecosystems are managed, how biodiversity and ecosystem services are affected, and to what extent human demands are met. Large-scale economic and land-use models
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Join the Laan Lab at TU Delft to unravel the mesoscale physics of how cells maintain robustness through evolution using live-cell imaging and multiscale modelling. Job description Cells are often