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10 Jul 2026 Job Information Organisation/Company Delft University of Technology (TU Delft) Research Field Engineering » Civil engineering Engineering » Computer engineering Researcher Profile First
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under global change requires accurate and consistent soil information at global scale. Current global soil maps are derived using empirical machine learning that often ignores known soil processes
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of high-quality GPR data collected at the University of Twente’s Utility Mapping Site (UMS), a unique test environment for utility mapping technologies. Current machine learning models and their training
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environment for utility mapping technologies. Current machine learning models and their training data are limited in size, comprehensiveness, and realism – resulting in partial automation with limited
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also include generative or predictive modeling of dynamic radar scenes. The project combines methodological machine learning research with experiments on real automotive sensor data. You will have access
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expertise in artificial intelligence, computer vision, human-computer interaction, and psychology. Its technical core lies in developing robust and adaptive visual speech recognition models. Close
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compositionally complex recycled steels, using density functional theory and machine-learned interatomic potentials, in close collaboration with leading academic partners and Tata Steel. Job description At TU Delft
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microstructure in materials. A distinctive element of the project is its data-driven approach. Together with colleagues at Saxion University of Applied Sciences, you will contribute to the development of machine
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planning and decision-making. Working with real-world data from Alliander, you will publish at leading machine learning venues while building tools with tangible impact on the Dutch energy sector. The Dutch
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closely with colleagues and students, you’ll apply mathematics to tackle societal challenges and bring new data-driven modelling, experiment, and computer-aided mathematics into education. We welcome