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for a: PhD Candidate in Emotionally and Socially Aware Natural Language Processing (1.0fte) Project description Current Natural Language Processing (NLP) systems, and especially large language models
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. Foreseen challenges include data harmonization, key relationship extraction, scalability, metadata structuring, predictive modeling, and data quality assurance. The research will integrate structured and
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for pavement lifetime prediction, primarily in Dutch conditions. Foreseen challenges include data harmonization, key relationship extraction, scalability, metadata structuring, predictive modeling, and data
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nitrogen losses). Research methods will include statistical analyses, modelling and potentially farm interviews. Your team Together with pioneering Dutch farmers, the ReGeNL project is working to build
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description Current Natural Language Processing (NLP) systems, and especially large language models (LLMs), are interacting with human emotions more readily than earlier AI systems, but we still lack frameworks
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) Project description Current Natural Language Processing (NLP) systems, and especially large language models (LLMs), are interacting with human emotions more readily than earlier AI systems, but we still
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income) and related environmental impacts (for example changes in soil carbon and/or reduced nitrogen losses). Research methods will include statistical analyses, modelling and potentially farm interviews
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of topics include algorithmic fairness in network analysis, developing network embedding frameworks for real-world network datasets or AI models based on agentic LLMs for simulating real-world network data
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evolved into a mature technology with a broad spectrum of use cases and a diversity of platforms and building blocks. The foundry model is now becoming established where Process Design Kits (PDKs) allow
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a broad spectrum of use cases and a diversity of platforms and building blocks. The foundry model is now becoming established where Process Design Kits (PDKs) allow fabless designers to create