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dynamics. Particular emphasis is placed on opinion dynamics as well as distributed problems in coordination, optimization, and learning. The research encompasses both theoretical and computational aspects
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240 credits, at least 60 of which must be in advanced courses in Electrical Engineering, Computer Science, or Applied Mathematics. Alternatively, you have gained essentially corresponding knowledge in
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distributed computational pipelines and optimizing communication costs. You will also contribute to the integration and testing of the models in real D-MIMO environments, in close collaboration with a PhD
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(e.g., model compression/simplification and hardware-aware optimization). We are also interested in how resource-efficiency interacts with broader sustainability aspects of machine learning such as
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formation and how local dose is distributed. In the longer perspective, this knowledge will support optimization and translation of bioelectronic implants towards clinical application. In this project, you
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such as healthcare, finance, cy- bersecurity, and autonomous vehicles, where they interact dynamically with external knowledge sources, retain memory across sessions, and autonomously generate responses and
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inference and deployment costs (e.g., model compression/simplification and hardware-aware optimization). We are also interested in how resource-efficiency interacts with broader sustainability aspects
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. The employment requires strong subject knowledge in optimization, mathematical modeling, and quantitative analysis. You are a problem solver who works well with complex issues, understands complicated written