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evaluation under regulatory constraints (e.g., AI Act considerations). You also possess: experience with machine learning and model development; affinity with explainable AI, recommender systems, or hybrid AI
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with modern machine learning. You will work on extending data-driven models with process-informed constraints and novel data integration strategies. The position is embedded in the Computational
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11 Apr 2026 Job Information Organisation/Company Delft University of Technology (TU Delft) Research Field Engineering » Computer engineering Engineering » Systems engineering Researcher Profile
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partners all over the Netherlands for a 4-year research position that bridges human-computer interaction, computer science, design, and behavior change. Information In the Netherlands, almost 300.000 people
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-scale compound drivers. We will leverage machine learning methods to bridge the gap between drivers at coarse model resolutions and impacts captured by high-resolution observations. Job description Arctic
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). The field of Machine Learning on Graphs aims to extract knowledge from graph-structured and network data through powerful machine learning models. Designing provably powerful learning models for graphs will
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supervision signals (e.g., labels in a downstream task or symbolic constraints). You will perform machine learning research, developing a framework for learning interpretable and robust concepts with
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to the adaptation of the Environmental Noise Directive for these new technologies. Your main focus will be to develop machine learning-based drone noise models that will be able to generate an accoustic footprint
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applicants should have a strong academic record with a solid background in Machine Learning. Knowledge of Vision-Language-Action models and Novel View Synthesis techniques is a strong plus. Good programming
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sizes and frequencies by: Measuring rock fractures from UAV data using manual and automated mapping approaches (e.g., machine learning, convolutional neural networks). Monitoring physical weathering