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research problem in its own right, advancing both the conceptual foundations and computational tools for assessing NLP systems in ways that are reliable, valid, and human-centred. Application domains include
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. The research will integrate external knowledge and computational argumentation to produce reliable, persuasive, and ethically grounded responses, while also addressing the moral values and motivations of users
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introduce processing artifacts, creating a direct barrier to robust and reliable AI classification. AI4IV’s mission is to address these limitations and become a leader in AI for vision by providing
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and bandwidth usage; iii) investigating how explainability and robustness can be maintained in compressed models deployed at the far edge, ensuring trustworthiness and reliability in real-world
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and simulation techniques are necessary to develop Urban Digital Twins able to manage this complexity and produce reliable predictions. The candidate will be requested to contribute to this research
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with familiarity with foundation models (e.g. vision language models) and the ability to design and prototype innovative, reliable and reproducible solutions for complex 3D scene understanding tasks