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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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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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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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to develop intelligent solutions in computer vision, robotics and control. We work closely with industrial and academic partners to bring cutting-edge algorithms onto real machines and into real environments
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Offer Description Your Responsibilities Qualitative analysis of choice architectures and recommendation algorithms of social media platforms in the context of their communicative function within
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to develop intelligent solutions in computer vision, robotics and control. We work closely with industrial and academic partners to bring cutting-edge algorithms onto real machines and into real environments
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in Physics, Mathematics, Computer Science or a closely related field, ideally with a strong focus on quantum computing and numerical simulations. • Experience in data-reuploading quantum algorithms