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to investigate how such systems are designed and how they shape real-world decisions. Your job AI systems increasingly inform decision-making across organisational contexts, yet their representational role remains
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and responsible artificial intelligence, combining areas such as reinforcement learning, social and cognitive computational modelling, knowledge representation and reasoning, agent-based modelling. More
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, to create a unified and reliable representation of structural integrity. The work expands on TU/e’s contributions by developing algorithmic components for detection and classification of defects and anomalies
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representation, regulation, and access intersect. Through multi-sited qualitative ethnography at selected media-related and popular-culture heritage sites across Europe and its overseas countries and territories
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representation, regulation, and access intersect. Through multi-sited qualitative ethnography at selected media-related and popular-culture heritage sites across Europe and its overseas countries and territories
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of large neural models, and secondly to analyse and explain the representations that these systems are using. As a PhD candidate, you will conduct independent research in Neurosymbolic AI. This continues
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surrounding diversity, inclusion, public accountability, and cultural representation. In a context of shifting policy environments, public scrutiny, and evolving expectations from communities and stakeholders
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conduct independent academic research within the framework of the project and work towards a doctoral dissertation. Your research will examine the contemporary cultural and touristic representation of witch
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the framework of the project and work towards a doctoral dissertation. Your research will examine the contemporary cultural and touristic representation of witch persecutions in the Netherlands and explore how
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representations with limited, weak, or noisy supervision Adapting, specializing, or probing large pre-trained models for domain-specific visual understanding Self-supervised and representation learning for images