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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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of central nervous system (CNS) disorders, aiming to identify glial-derived determinants of disease trajectories as potential therapeutic targets. We combine cutting-edge biomolecular approaches with high
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, please submit your application, including all relevant documents, to the following email address: application.cms@ed.tum.de The application closes on December 8th , 2025. Targeted start date is February
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project and work environment: This research project addresses the profound impacts of environmental pollution on human health, specifically targeting how air- and foodborne pollutants may impair taste
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, engineering, data science, and computer science. Skill Development: Our extensive qualification concept goes beyond research, offering targeted training in research methods, project management, and leadership
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, adversarial attacks, and Bayesian neural networks. Excellent analytical, technical, and problem-solving skills Excellent programming skills in Python and PyTorch including fundamental software engineering
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properties. The position is part of the Bavarian 6G Future Lab, the pilot project of which is a collaboration of 13 research teams at TUM, targeting research into novel foundational mechanisms for future 6G