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Learning with Graphs led by Prof. Nils M. Kriege. Our research focuses on the development of new methods and learning algorithms for structured data. Graphs and networks are ubiquitous in various domains
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the mechanistic and evolutionary drivers of patterns and processes across biological scales, from gene regulation to ecosystem structure and function, including the role of communities in biogeochemical cycles and
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, the group holds significant expertise in the analysis of transit light curves and of radial velocity time series, as well as in the determination of stellar parameters from evolutionary models. The group is
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and learning algorithms for structured data. Graphs and networks are ubiquitous in various domains from chem- and bioinformatics to computer vision and social network analysis. Machine learning with
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the mechanistic and evolutionary drivers of patterns and processes across biological scales, from gene regulation to ecosystem structure and function, including the role of communities in biogeochemical cycles and
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research focuses on the development of new methods and learning algorithms for structured data. Graphs and networks are ubiquitous in various domains from chem- and bioinformatics to computer vision and
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applied research for digital security and is a pioneer in developing highly innovative concepts, algorithms and systems for anomaly detection, cyber threat intelligence, penetration testing and cryptography
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in applied research for digital security and is a pioneer in developing highly innovative concepts, algorithms and systems for anomaly detection, cyber threat intelligence, penetration testing and
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. Specifically, our Responsive Sensing and Analyticsteam is developing efficient and scalable algorithms to provide emergency services with the necessary safety-relevant information and analysis results based
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professional field of activity: The selected candidate will be part of the 'Evolutionary Morphology Research Group' (EvoMorRG; Chair Palaeobiology with Special Emphasis on Vertebrate Palaeontology