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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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high-quality data through functional genetic screening approaches with the aim of enabling novel therapeutic strategies. For further job related details see link below: Tenure track professorships Where
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to new ideas and hypothesis An active and international PhD program Possibility to discuss ideas with experts in the group for secondary metabolite analysis, molecular genetics and Trichoderma species
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of biology First practice in scientific writing and with molecular-genetic research methods Didactic skills / experience in e-learning Strong IT user skills, including MS Office and other programs e.g