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learning, small data learning · Active learning, Bayesian deep learning, uncertainty quantification · Graph neural networks This position involves active participation in a well-funded
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Applications are invited for a 2-years position as postdoctoral researcher within the research project “Graphs and Ontologies for Literary Evolution Models” (GOLEM), financed by an ERC Starting
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Information Modeling (BIM), including Revit, IFC, ifcopenshell, compliance checking, design generation, and design checking; and Experience in AI and LLM-related development, including RAG, knowledge graph
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, computational fluid dynamics and material science, dynamical systems, numerical analysis, stochastic problems and stochastic analysis, graph theory and applications, mathematical biology, financial mathematics
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graph theory. Qualifications Candidates with a Ph.D. in any area of cognitive neuroscience broadly defined (e.g., Psychology, Neuroscience, Computer Science, or a related field) are welcome to apply
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real-world applications in green chemistry and industrial synthesis. Key Responsibilities: Develop and implement AI/ML models (e.g., graph neural networks, transformer-based models) for retrosynthetic
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include: Strong foundation in one of the following areas: Machine Learning / Information Retrieval / Knowledge Graph Representation / Recommender Systems Graph Theory/Network Science Python, and up-to-date
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include: · building hierarchical causal graphs to account for the multi-scale structure of the experimental system, · detecting latent variables that may affect causal inference
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the theory of quantum graph states. Additional expertise in computational methods would be useful but is not necessary. The Postdoctoral and Senior Research Associate positions will also involve
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to develop a knowledge-aware and event-centric framework for natural language understanding, in which event graphs are built as reading progresses; event representations are learned with the incorporation