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implement computational frameworks for processing, integrating, and analyzing large-scale phosphoproteomics patient data, supporting the discovery of signaling networks and actionable therapeutic targets in
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biomolecular structure, dynamics, and interactions in complex environments and live mammalian cells. By combining ultra-high-field NMR with complementary biochemical, computational, and cellular approaches
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ENGAGE Network at TUM and GIM Robotics. About the ENGAGE Network Mobile working machines (MWM) are critical to industries like construction, mining, and agriculture, and key to Europe’s sustainability and
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the Stiftung Innovation in der Hochschullehre. LEAP aims to develop personalized learning paths that prepare students for a dynamic and complex professional world. In this role, you will coordinate
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storage Innovation in the Machine Learning algorithms for EDA in terms of Computational Complexity, Performance Scores, etc. To learn more about our previous work, please check out our website
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and to be able to use all algorithms in the real vehicle. By taking a particular look at complex scenarios, such as driving dynamic limits, unstructured environments or problems from ethics, we extend
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programming skills in a higher programming language (e.g., Python, Java). You already work with traffic simulations and digital twins, or they spark your interest. You break down complex topics by approaching
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). AI methods, and especially machine learning (ML) with deep neural networks have replaced traditional data analysis methods in recent years. The Technical University of Munich (TUM), together
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Learning in Earth Observation (ML4Earth). AI methods, and especially machine learning (ML) with deep neural networks have replaced traditional data analysis methods in recent years. The Technical University