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activities, such as assisting in courses of our computing science programmes. Would you like to learn more about what it’s like to pursue a PhD at Radboud University? Visit the page about working as a PhD
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reinforcement learning (RL), active learning, Bayesian decision theory, and stochastic optimisation for partially observed and evolving systems. Key research directions include: adaptive data acquisition
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, such as assisting in courses of our computing science programmes. Would you like to learn more about what it’s like to pursue a PhD at Radboud University? Visit the page about working as a PhD candidate
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activities, such as assisting in courses of our computing science programmes. Would you like to learn more about what it’s like to pursue a PhD at Radboud University? Visit the page about working as a PhD
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support. The PhD candidate will develop and validate a hybrid methodology that combines established stochastic optimization with AI-based learning. The aim is not only to develop new algorithms, but also to
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actively on the preparation and defence of a PhD thesis in the field of explainable reinforcement learning (XRL). Explainable reinforcement learning aims to make decisions, policies, and learning processes
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actively on the preparation and defence of a PhD thesis in the field of continual reinforcement learning. Continual reinforcement learning studies how agents can learn across a sequence of changing tasks
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DTU Management invites applications for a 3-year PhD Scholarship associated with the project “Brains in Motion: Explaining How Humans Learn and Adapt to Navigate Urban Environments Using Artificial
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thesis in the field of continual reinforcement learning. Continual reinforcement learning studies how agents can learn across a sequence of changing tasks, environments, or objectives while retaining
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and large models, limiting real-world deployment. This PhD focuses on efficient Physical AI, emphasising data-efficient training, reinforcement learning, continual adaptation and edge deployment