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) at the Technical University of Munich (TUM) is looking for a talented postdoctoral researcher (f/m/d) to deepen their expertise and interest in machine learning for medical image analysis and built their early
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channels, going beyond the Shannon paradigm, which offers many exciting open questions to work on. Thirdly, the project aims at investigating non-Shannon-type inequalities for the quantum entropy, which in
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simulators (on various abstraction levels using, e.g., Computational Fluid Dynamics) which enables us to verify designs of microfluidic devices even before the first prototype is fabricated. Fabrication: We
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with such environments. We investigate machine learning approaches to infer semantic understanding of real-world scenes and the objects inside them from visual data, including images and depth/3D
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of microfluidic devices even before the first prototype is fabricated. In this field, we are involved in a consortial project with stakeholders from academia and industry to establish those tools for practical
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with such environments. We investigate machine learning approaches to infer semantic understanding of real-world scenes and the objects inside them from visual data, including images and depth/3D
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interactions with such environments. We investigate machine learning approaches to infer semantic understanding of real-world scenes and the objects inside them from visual data, including images and depth/3D
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in static and dynamic 3D reconstruction, semantic scene understanding, and generative models for photo-realistic image/video synthesis. Overall, the main focus is on high-impact research with the aim
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skills, ability to interact with scientists at different levels good software design skills and the ability to write clean, and reusable code in machine learning, deep learning frameworks, such as