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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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for compositional methods that focus on extra-functional aspects, such as performance, resource budgets, security, or energy, pursuing hybrid, knowledge-driven and machine-learning-based, approaches. As a newly
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are the use of challenge-based learning, individualized learning paths and development of professional and personal skills. We are looking for a university teacher to strengthen the team and to teach our
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machine learning; a minor in mathematics is a plus; A strong interest in data mining research with focus on local pattern mining, exceptional model mining, or related techniques. Data mining software
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expertise ranges from cryptanalysis to machine-checking cryptographic proofs. To get an impression of our research, visit the cluster website as well as the personal websites of our team members. We have
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interaction to establishing correctness of software. We play a crucial role in both bachelor and master programs on Computer Science and Engineering and support bachelor programs in Data Science, Mathematics
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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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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