16 machine-learning-modeling PhD positions at Delft University of Technology (TU Delft)
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the Computer Engineering group. Curious to learn more about the project, and perhaps our group? Feel free to browse our webpages: About our department: QCE department . About our group: Computer Engineering Lab
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-modal representation learning, and self-supervised learning for this novel perception task. The developed models should provide holistic representations of all surrounding traffic by fusing multi
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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
Infrastructure? No Offer Description Develop machine learning models to detect early signs of abrupt shift towards clean energy technologies and make climate action adaptive to this information. Job description
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. Until now, specific EN fingerprints of localized corrosion are determined manually. This is a tedious procedure that requires considerable expert knowledge. Artificial intelligence or machine learning (AI
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each other. This necessitates a multidisciplinary approach bringing together optimization, machine learning and behavioral modeling methodologies. In FlexMobility we propose a holistic approach to design
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guarantees. This includes working with techniques such as differential privacy and PAC-privacy to enable safe model and explanation release. Familiarity with privacy-preserving machine learning methods is a
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24 Nov 2025 Job Information Organisation/Company Delft University of Technology (TU Delft) Research Field Engineering » Aerospace engineering Engineering » Computer engineering Researcher Profile
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make extensive use of low-fidelity simulations which can provide fast but inaccurate solutions depending on the flow complexity. To close this gap, this PhD will explore machine-learning (ML) methods
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expert knowledge in a reusable format. Numerical Representation, Develop numerical representations of ship designs that are interpretable by machine learning algorithms and suitable for generative ai model
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. This simple unit is limiting the learning capabilities of recurrent neural network models in tasks characterized by multi-timescale and long-range temporal dependencies. To implement multi-scale adaptation, in