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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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theoretical research, algorithm design, and the development of software tools that demonstrate the applicability of the new methods. Research environment The positions are hosted by the Department
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build the sustainable companies and societies of the future. Pervasive and Mobile Computing (PMC) research subject addresses fundamental challenges in distributed systems and mobile networks, enabling
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build the sustainable companies and societies of the future. Pervasive and Mobile Computing (PMC) research subject addresses fundamental challenges in distributed systems and mobile networks, enabling
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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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modular, scalable, and transparent control algorithms suitable for real-time implementation across different vehicle platforms. - Contribute to theoretical developments in stochastic model predictive
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operation Quantum algorithm implementation and benchmarking About you You have a relevant Masters deegree corresponding to at least 240 higher education credits (Physics, Nanotechnology, Engineering, Computer
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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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well as topics in phylogenetics. This project will involve working closely with experimentalists, and will be co-supervised by Prof Gerald McInerney and Dr Daniel Sheward, who have expertise in virology and
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contact: Prof. Eduard Hryha, director of CAM2 and topic leader: hryha@chalmers.se Prof. Uta Klement, head of Division of Materials and Manufacture: uta.klement@chalmers.se We look forward to your