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Vacancies PhD position on Dependability Driven on Device Learning Algorithms for Embedded Neuromorphic Architectures Key takeaways Edge devices that can learn autonomously while guaranteeing
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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, 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
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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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. The proposed research examines judgment accuracy in the intensive care unit (ICU), You will investigate whether algorithmic advice can improve clinicians’ judgments, whether independent judgments or peer
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with established causal models. Ultimately, you will design algorithms for causality-based analysis and counterfactual recovery of liveness violations. Information and application Are you interested in
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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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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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. 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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consumption, create energy labels for algorithm scalability, and guide implementers in choosing more efficient algorithms. Ready to make AI more sustainable? Apply now! The goal of your PhD project is to