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use, and long-term crop management goals. While several algorithms have shown promising results in energy savings and crop yield, most of these methods have only been tested in simulation, and make use
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, algorithms, and tools for human-centered intelligent realities, to lead the way for future immersive, user-aware, and smart interactive digital environments. The project is divided into five separate Research
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of continual graph learning. Continual graph learning studies how graph neural networks can learn from a sequence of evolving tasks, graphs, or distributions while retaining previously acquired knowledges
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thesis in the field of continual graph learning. Continual graph learning studies how graph neural networks can learn from a sequence of evolving tasks, graphs, or distributions while retaining previously
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for 3-year PhD positions in computer science with emphasis on formal methods. Possible topics include (but are not limited to) computational complexity, distributed systems, human factors, logic
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investigate the influence of trajectory accuracy on player perception and engagement within a dedicated laboratory environment. Developing distributed synchronization algorithms: we will design and implement a
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an increasingly complex, fragmented and distributed news media environment. This includes examining how people encounter, consume and engage with news across digital platform s, social media and new technologies
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research on post-quantum cryptographic algorithms and protocols, assisting in the design, implementation, testing, and evaluation of secure cryptographic solutions resilient to quantum attacks, and
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energy resources. The expected outcomes include technical advancement of distributed algorithms for managing energy resources at customer premises. The benefits include more resilient, secure, private, and
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- and long-term forecasting. Design hybrid AI architectures and optimize them using advanced techniques such as attention mechanisms and evolutionary algorithms. Implement probabilistic and explainable AI