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Field
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Trustworthy Graph Machine Learning for Population Scale Networks Job description We invite applications for a postdoctoral researcher to work on fundamental techniques for trustworthy graph machine
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future. This post-doctoral position will investigate the use of knowledge graphs on automatically extracted metadata at a cross-disciplinary global scale. Using LLMs and traditional data engineering
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The recruited researcher will work within the framework of the ANR project Ordered Graphs, Decompositions, Algorithms and Structures (GODASse). Due to their simple definition and versatility in modeling complex
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taxonomies and by extracting data using advanced Natural Language Processing techniques, a knowledge graph of the Romanian labor market will be created. Through an Agent-based Modeling approach the dynamics
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extracting data using advanced Natural Language Processing techniques, a knowledge graph of the Romanian labor market will be created. Through an Agent-based Modeling approach the dynamics of the labor market
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and Mathematical Statistics at Umeå University (Sweden) is offering a postdoctoral scholarship within the project “Bounds for Pancake Sorting and Analysis of the Pancake Graph”. The scholarship is tax
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technology (graph neural networks and transformers) with a dynamical systems framework. The dynamical systems viewpoint imagines the turbulence as a space-time tapestry of connected states, which are related
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, higher-order network methods and large-scale language-model training on a knowledge graph of scientific publications, patents and related innovation data. The appointed researcher will lead DII's
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track record in one or more of the following areas, or a combination of these areas: Knowledge-grounded artificial intelligence and knowledge graphs, including large language model (LLM) and retrieval
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summarizing results, trends, and interpretation. Prepare visuals (graphs, micrographs, charts) for internal meetings and publications. 6. Collaboration and interdisciplinary communication Work in an