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Field
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Graph Machine Learning and Graph Data Management At Section for DATA, Department of Computer Science, Aalborg University, a postdoc position is available. The project is funded by a Novo Nordisk
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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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use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and
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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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generative models and/or graph neural networks. Domain knowledge of Mechanical Engineering problems is not required. The ability to transfer your knowledge to Engineering problems is however expected. Please
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on Topological methods in Discrete Mathematics and conduct research related to problems in Combinatorics, Graph theory and aspects of the Constraint Satisfaction Problem (CSP) with emphasis on topological methods
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collaboration with Growgraph, an R&D startup engaged in advancing knowledge graph technologies and AI-driven methods for structured data analysis. This partnership aims to foster the transfer of research outcomes
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networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our
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and optimization, we use tools such as artificial intelligence/machine learning, quantum conputing, graph theory, graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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the PI in writing scientific papers based on research findings. This includes generating graphs, tables, and other visuals for internal reports, grant applications, and publications. Presents research