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your academic career? Your interests lie in the field of machine learning techniques, particularly artificial neural networks, and deep learning? And you would like to continue your research
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theoretical challenges motivated by astrodynamics, optimisation, control, scientific machine learning, mission design and autonomous systems, translating these into new research directions in deep learning
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across a range of application areas, including education and healthcare. As these systems are increasingly deployed in high-stakes environments, there is a growing need for machine learning models
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economy. As a postdoctoral researcher, you will unravel how silicon suppresses liquid copper infiltration at the atomic scale, using density functional theory-accurate machine-learned potentials and
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at the atomic scale, using density functional theory-accurate machine-learned potentials and molecular dynamics simulations, in close collaboration with leading European research institutes and steel industry
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partner who focuses on the underlying machine learning models, and you will help coordinate the joint work across the two teams. The position offers a strong publication trajectory at leading HCI venues
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-edge machine learning methods with empirical insights from the educational arm of the project. A central technical challenge guides this position: How can an LLM-based AI social agent be designed, fine
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energised — not deterred — by problems that sit between physics, learning and the messy real world. Your experience and profile: a PhD (completed or near completion) in Machine Learning, Computer Vision
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for reasoning, on efficient and explainable machine learning for extracting and structuring information from large datasets, and on combining the two in neuro-symbolic AI.
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cardiovascular care. Within the consortium, TU Delft contributes expertise in cardiac mechanics, soft tissue modeling, growth and remodeling, machine learning, and uncertainty-aware model personalization. As a