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This PhD project focuses on strengthening network security for large-scale distributed AI training. As training increasingly spans multiple data centers connected over wide-area networks, it
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, coordination, and decision-making algorithms for multiple autonomous agents—such as robots (robotic manipulators, drones, or vehicles)—that work together to achieve common goals in dynamic, uncertain
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algorithms to detect complex structural variants in humans using long DNA sequencing reads. A structural variant (SV) is a large-scale alteration in the genome that involves rearranged, deleted, or inserted
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of scientific data, e.g. from image acquisition modalities or scientific simulations. Efficient algorithms are at the core of most of these data analysis and visualization applications. The focus of this Ph.D
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using state-of-the-art single-cell omics technologies. The team consists of the principal investigator, two experimental scientists (doctoral students), one bioinformatician (postdoc), and one
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Technology Laboratory (QTL) division of the Microtechnology and Nanoscience (MC2) department, working in a large team of PhDs, postdocs and researchers. About the research We are seeking PhD students to work
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and application architectures with implementations of massively distributed embedded systems that interact with each other and their environment to enable secure, goal-driven, autonomous and evolvable
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safety. You will work on developing control algorithms all the way to performance assessment in test vehicles. The project combines theoretical aspects of control algorithms, experimental design, and
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industrial partners and is partly externally funded by the KK Foundation. In co-production with our corporate partners and the community, we develop concepts, principles, methods, algorithms, and tools
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than 260 PhD students and 200 postdocs will be part of the Research School. The DDLS program has four strategic research areas: cell and molecular biology, evolution and biodiversity, precision medicine and