169 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "University of Waterloo" positions at Technical University of Munich in Germany
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on advanced machine learning and emulation approaches. Key responsibilities: The candidates will be expected to work on the following tasks: - Develop machine learning (ML) methodologies appropriate
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Bayesian machine learning to improve risk management for bridge portfolios. We offer a funded PhD position in an excellent research environment. The project Our infrastructure is aging, and decisions about
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9 Feb 2026 Job Information Organisation/Company Technical University of Munich Department Computer Engineering Research Field Technology » Communication technology Researcher Profile First Stage
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our team at the TUM on the ERC project Learning Matters!. Task You will implement learning mechanics in soft matter, specifically in biocompatible hydrogels. Your hydrogels will form the walls of a
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related discipline. Strong expertise in medical imaging and/or machine learning. Excellent programming and research skills. Interest in translational research and interdisciplinary collaboration with
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-friendly frameworks for multimodal elastography and enabling deployment on portable devices (e.g., smartphones) for real-time diagnostics. The research combines continuum mechanics, machine learning
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approaches are gaining importance for autonomous vehicles. However, the training and certification of autonomous systems with machine learning components is a huge challenge, since the learned behavior is
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project Learning Matters!. Task You will break with the current focus on the brain to uncover the physics of continual learning instead by investigating the emergence of learning bottom-up in life, reduced
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skills in Python, Java, C++, etc. A solid foundation in generative AI, machine learning, and related areas. An Interest in eye-tracking technology, Computer Vision, Speech/ Language Processing, VR, and AR
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tailored computational methods are needed. This project aims at combining probabilistic machine learning methods with prior knowledge in the form of graphs to analyze and predict food-effector systems. Key