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We invite you to apply for a 2-year postdoctoral position in the area of hybrid AI, combining modern language models (LMs) with ontologies and knowledge graphs (KGs). The position is part of a newly
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humanities, knowledge graphs, ontologies, graph databases, network analysis, natural language processing, computer vision. They should also have a PhD in one of the following areas: Digital Humanities
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computer science with very good results - Interest on topics around the area of distributed systems and data management - Basic knowledge in distributed systems and graph algorithms is desired - Hand-on experience
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and machine learning. Topics of interest in this area include, but are not limited to: natural language processing, large language models, graph learning, prompt engineering, knowledge graphs, knowledge
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are not limited to: natural language processing, large language models, graph learning, prompt engineering, knowledge graphs, knowledge engineering, linked data, web technologies. About the role
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role Research in the general domain of stochastic analysis, with special focus on stochastic geometry, such as random fields, random graphs and related structures, limit theorems, stochastic calculus and
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are not limited to: natural language processing, large language models, graph learning, prompt engineering, knowledge graphs, knowledge engineering, linked data, web technologies. About the role
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censorship. Core Responsibilities Develop LLM-driven knowledge graphs that construct probabilistic historical priors from bibliographic records, trial transcripts, censorship lists, and apprenticeship data
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researcher will work at the interface of root developmental biology, 3D modeling, network and graph theory, and data analysis, in close interaction with biologists, modelers, and computer scientists (INRAE
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conclude on December 31st 2029. The goal of this research effort is to apply machine learning (ML) techniques, in particular (equivariant) graph neural networks to accelerate the creation of all physical