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develop methods to examine the process by which AI-models solve problems that were previously considered to require human-like (fluid) intelligence, e.g., abstract and analogical reasoning problems, and to
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learning algorithms. Personalizing user interactions by building models that adapt explanations to specific knowledge levels and interests of users, so that user modelling and formal reasoning transform
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explainable AI (XAI) methods with user-centred interaction design, combine machine learning with alternative AI methodologies (e.g., rule-based reasoning, knowledge graphs, hybrid approaches where relevant
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. Personalizing user interactions by building models that adapt explanations to specific knowledge levels and interests of users, so that user modelling and formal reasoning transform opaque decision-making
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in pollutant fate, microbial processes, and long term environmental impacts A central knowledge gap concerns whether indigenous anaerobic microbial communities can transform persistent pollutants
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this specific structured data. How can we perform inference tasks to learn hidden patterns, like community structure or hidden hierarchies? How can we incorporate domain knowledge to design interpretable models
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broad Bachelor's and specialist Master's programmes with a strong research component. Our scientists conduct ground-breaking fundamental and applied research in the fields of Life and Health Science
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of AI by integrating causal reasoning? The Causality team in the AMLab group at the University of Amsterdam is looking for 2 PhD students to work on this topic in the NWO VIDI sponsored project CANES
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Passports (DPP) for compliance monitoring for steel circularity and the green steel transition. Specific aspects will be business-government partnerships. Many businesses introduce for business reasons and
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approaches, can easily solve -- particularly involving abstract and flexible reasoning. There is therefore a need to integrate symbolic and neural approaches, firstly to potentially improve the performance