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operations. The goal of this project is to conceive, develop, and evaluate optimization models and algorithms for increasing the economic efficiency of last-mile delivery operations, including routing
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communicating and sharing information. Your job In this project, we focus on interpretability of the communication in MARL algorithms. We aim to bring together causality and multi-agent reinforcement learning
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how children (8-12), adolescents (12-18) and young adults (18-25) build critical literacy skills to evaluate news in the context of algorithmic personalization and GenAI. It brings together expertise
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advanced motion planning algorithms with machine learning techniques, such as reinforcement learning, imitation learning, and task generalization. You will focus on designing intelligent robotic systems
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algorithms for AI agents to learn, apply, and generate justifications in collaborative settings. You will be co-supervised by Davide Dell’Anna (Utrecht University) and Myrthe Tielman (Delft University
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. Your work will focus on design-space exploration and optimizations tailored to different environments. The optimizations range from algebraic optimizations (e.g., term rewriting), to algorithmic
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to the above mentioned topics, using a combination of techniques ranging from mathematics, algorithm design and implementation, to computer experiments and design research; publish, present and share your
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are you up for a challenge? Are you motivated to contribute to solving important societal questions by developing algorithms, adjusting existing statistical models and conducting simulation studies
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critical literacy skills to evaluate news in the context of algorithmic personalization and GenAI. It brings together expertise on media and journalism studies, digital literacy and inclusion, argumentation
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algorithms, focusing on SNN few-shot, synergic (local-global) and asynchronous learning strategies suitable for real-time and embedded systems scenarios. Finally, you will benchmark the developed system