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process. By integrating simulation-informed priors into the reconstruction algorithm, we aim to significantly improve both accuracy and sensitivity. The position is hosted at the Fritz-Haber-Institut
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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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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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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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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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healthcare, in most cases, there is only access to information at the patient level, about the patient’s health status and disease development. In this project, we will develop theory, algorithms and methods
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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