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starting date is November 2025. The topic of the PhD project will be theoretical research in discrete optimization, with a particular focus on either graph algorithms or multiobjective optimization
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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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) 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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. - 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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application focus: Design knowledge-graph-augmented transformers and retrieval-augmented generation (RAG) pipelines that enable semantic querying and reasoning over materials-science/physics corpora Developing
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shifts in cell state and cell fate. Integrate spatial transcriptomics data to anchor these predictions in tissue context. Develop machine learning methods (e.g. graph neural networks, variational
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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