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methods to make them usable for transparent energy systems analyses. The collected data will be processed and semantically enriched using methods you develop before being transferred to a knowledge graph
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[2, 4, 5]; - Lack of common representation for process flowsheets (graph incidence matrix, custom-made dedicated language, SFILES 2.0 standard) - Various custom-made process simulation environments
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and advancing techniques such as machine learning, graph-based network analysis, and synthetic data generation, the project tackles key challenges in anomaly detection, transaction classification, and
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. - Compilation of a curated catalog of archaeal genomes from public data and newly obtained data within the team. - Orthogroup inference, multi-clade pangenome graphs to detect genes with restricted distributions
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interaction and/or web programming, and human-computer interaction is required. Experience and/or knowledge of semantic web technologies, such as ontologies and semantic web standards, as well as graph data and
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, turning geodata into new answer maps. We use knowledge graphs to model these transformations and apply AI methods to scale them across large map repositories, enabling users to explore many ways maps can be
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SQL databases and file repositories. We are now taking the next strategic step: developing ontologies and a dynamic knowledge graph to semantically link our internal data systems - and connect them
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) have some exposure to (hyper)graph theory, network science, and/or reaction mechanism/CRN studies. Candidates who do not meet all of these criteria should not feel discouraged. If you are interested in
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methods you develop before being transferred to a knowledge graph-based metadata platform that you will help develop. In collaboration with stakeholders from energy research, you will develop methods
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of convolutional neural networks, graph neural networks, and attention-based architectures, with the attention mechanisms explicitly guided by the physical principles and intrinsic properties of the atmosphere