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Field
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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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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 from
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of dynamic physical systems and control system algorithms experience in software development, data analysis and AI practical experience in experimental work motivation and potential for research within
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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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setting, enabling fast and predictable adaptation with minimal overhead. A central focus is the co-design of algorithms with edge hardware and embedded platforms. You will investigate implementation
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on practical feedback linearization with limited or imperfect models. Learning-enabled control dynamics Embedding optimization and learning algorithms (e.g., SGD, Bayesian updates) into control design and
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will also collaborate with a postdoctoral researcher and another PhD candidate on creating new GPU-enabled pharmacophore searching algorithms. Prospective validation will be achieved by predicting
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theory? Do you want to develop cutting-edge control algorithms for the security and resilience of cyber-physical systems? We welcome you to apply for a PhD position in the SecReSy4You Doctoral Network, a
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of the screening output. This approach selects new, efficient enzymes, but also generates unique sequencefunction datasets that will be interpreted by regression and tree-based machine learning algorithms to obtain
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-edge academic insight with real-world robotics applications. The PhD project will focus on the design of advanced control and decision-making algorithms to enhance the intelligence, autonomy, and