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formalizes the synergy between physics, information theory, and machine learning, particularly focusing on computing with Oscillatory Neural Networks (ONNs). Project The project aims to formalize the synergies
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network and data centers, the transmission link optimization at the physical layer, and the computing decentralized systems by exploiting state of the art machine learning approaches to ultimately implement
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. The candidate is expected to strengthen the department’s competence in machine learning and AI. This position is intended to strengthen the department’s research and education program regarding gameful design
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emphasis in the areas of game design, virtual worlds, and digital twins. The candidate is expected to strengthen the department’s competence in machine learning and AI. This position is intended
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an accurate overview of the state of the art (intersection of urban planning, VR experiments, digital twining, big data and machine learning); as well as recent developments in this field of study in academics
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supporting professionals in complex tasks using high-dimensional data streams, providing new views on data using knowledge representation techniques and machine-learning, or connecting organizational data
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one of the top departments in the world that conduct exciting research in the intersection of Design, Technology, Human-Computer Interaction, and Social Sciences and Humanities. In particular
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theory Machine learning Optimization and game theory Data-based control PDE control Large-scale and multi-scale systems Knowledge on relevant application domains such as, for example mechatronics, high
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these limits. Main areas of interest are source coding, channel coding, multi-user information theory, security, and machine learning. We typically use information-theoretical frameworks to model the scenarios
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knowledge of other fields that are in high demand in the automotive sector, such as sensors, vision, machine learning, deep learning, artificial intelligence, advanced driver assistant systems, automated