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of machine learning (ML) and quantum many-body physics. We are also happy to work with experts in one of the two fields who are committed to learning the other. Moreover, we look for interest in developing
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limitations. The field of interpretable machine learning aims to fill this gap by developing interpretable models and algorithms for learning from data. Meanwhile, the field of knowledge discovery and data
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, the researcher will develop theory and algorithms for (hybrid) model selection that allows to exploit domain knowledge through interactive learning. For this we will build on the minimum description length (MDL
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of topics include algorithmic fairness in network analysis, developing network embedding frameworks for real-world network datasets or AI models based on agentic LLMs for simulating real-world network data