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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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network integration for emerging low-energy opto-electronic AI systems and beyond. The challenge: Machine learning and neural networks are super-charging the complexity of problems that computer algorithms
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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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of algorithmic systems. The research will investigate how clinicians interact with automated and machine learning–based decision-support systems, with a particular focus on cognitive workload, trust, situational
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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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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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this project, you will develop the next generation of federated machine unlearning algorithms—methods that can efficiently deliver genuine, verifiable, and robust erasure without sacrificing model performance
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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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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