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be placed on Representation Learning techniques, Transformer-based architectures, Large Language Models (LLMs), Natural Language Processing (NLP), etc. The research will also explore distributed data
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practices. The findings will serve as the empirical foundation for the security framework. Defensive Strategies: Propose and prototype new defensive architectures and techniques that can be integrated
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structured around two main pillars: Network resilience and sovereignty, i.e., research on networking architectures and mechanisms that keep critical networks and applications they support running optimally
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information, and case files Potential research topics for the Ph.D. project are: Adaptation of language model architectures and pipelines for high-stakes public sector Benchmark and comparative analysis methods
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multimodal data, dynamic updates, and scalable semantic interoperability in large-scale DPP systems. Particular emphasis will be placed on Representation Learning techniques, Transformer-based architectures
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scalable semantic interoperability in large-scale DPP systems. Particular emphasis will be placed on Representation Learning techniques, Transformer-based architectures, Large Language Models (LLMs), Natural
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. The planned PhD research topic will explore techniques to handle protected data in digital twin architectures. To explore and investigate this topic, the project will combine formal methods, programming