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the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics
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of Systems and Control, we develop both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms
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materials. This class of materials has unique properties which make them promising candidates for next-generation electronic devices, energy storage systems, sensors, and catalysts. However, they also pose
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of Systems and Control, we develop both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms
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to humans and are accessible to algorithmic techniques while neural models are adaptive and learnable. The aim of this project is to develop models which combine these advantages. The project includes both
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theoretical research, algorithm design, and the development of software tools that demonstrate the applicability of the new methods. Research environment The positions are hosted by the Department
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, Internet of Things, Systems-of-Systems automation, Machine Learning, Deep Learning, Data Science, Electronic systems design, and sensor systems. Cyber-Physical Systems (CPS) focuses on integrated software
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integrated sensor arrays. The project combines several different concepts: Progress in understanding insect neurobiology that provides proven circuit designs to solve significant problems such as autonomous
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from manufacturing sensors and XR headsets (video, audio, motion). This includes building multimodal AI pipelines, generating procedural representations, and contributing to the integration of real-time
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questions include automated modeling and model simplification/refinement supported by generative AI, system identification, and 3D reconstruction algorithms. Additionally, the research involves developing