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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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highly motivated and self-driven geoscientist that can enrich and strengthen the Department in subjects related to alluvial fan sedimentology, architecture, and petrography. The position will focus
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the Department in subjects related to alluvial fan sedimentology, architecture, and petrography. The position will focus on developing new concepts in catchment–alluvial fan dynamics within a structural framework
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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