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Field
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to the user, which pictures to show? A third possible topic is performance improvement of using a graph-based analysis and/or infrastructure. Typical RAG systems use a semantic search based on embeddings. NEO
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of graph neural networks on inverse design Experience with application processes in the EU and NFR Skills in Norwegian or another Scandinavian language Personal characteristics To complete a doctoral degree
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established practices? iii) Technology Assessment: To what extent do representational approaches such as ontologies or knowledge graphs meet the identified requirements, and where might alternative or hybrid
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tools without disrupting established practices? iii) Technology Assessment: To what extent do representational approaches such as ontologies or knowledge graphs meet the identified requirements, and where
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-driven forward and inverse design Experience in the construction generative artificial intelligence on material design Experience in the application of graph neural networks on inverse design Experience
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. Methodological Approach Candidates will develop and apply state-of-the-art machine learning techniques, including deep learning, representation learning, variational autoencoders, and graph-based models. A strong
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workflows, turning geodata into new answer maps. Knowledge graphs can be used to model these transformations and to link geodata sources to questions. In this project we will apply symbolic and sub-symbolic
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, such as graph-based approaches with Retrieval-Augmented Generation (RAG) and fine-tuning of LLMs. • Contribute to developing open-source tools and code repositories • Produce high-level scientific