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for downstream applications such as mapping, simulation, analyses and other related uses. This PhD investigates methods to transform raw 3D data into structured scene representations that integrate
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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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archaeological signatures (e.g., micro-relief, edge structures, etc.) – Design and implementation of new deep learning architectures (both supervised and unsupervised/few-shot, 2D and 3D) for an efficient and
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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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microsystems, mesoscale components, and printed electronic or structural elements, including those fabricated on flexible and/or biodegradable substrates, with transduction functionalities exploiting various
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, and the integration of compliance requirements into decision-making structures. It will combine doctrinal legal analysis with case studies and design-oriented methodologies in order to develop
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secondments, and structured training in transferable skills. The candidate will be enrolled in a recognized PhD programme and supervised by internationally leading researchers. The TRILOGY network focuses