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graph, and discrete random processes. The aim of this project is for the student to develop an understanding of these tools and to apply these techniques to open research problems in the field. Entry
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, storm activity, and other hazards using graph-based clustering, fuzzy machine learning, and reduced-order models – delivering scientific insight into where and when rerouting is needed. Real-Time Decision
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or term paper that you think best reflects your abilities A sample of code(s) which you have written, along with a summary of the outputs it generates (logs/graphs) in a single document We will be accepting
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, complex datasets from multiple sources Integrate and harmonize data using semantic data linking (ontologies/knowledge graphs) to create decision-ready information Design and prototype scalable workflows
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., symmetries), learning on structured domains (e.g., graphs, manifolds) (to achieve data efficiency and respect constraints) Uncertainty Quantification: building models that quantify uncertainty associated with
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Dortmund, we invite applications for a PhD Candidate (m/f/d): Multidimensional Omics Data Analysis You will be responsible for Setup a knowledge graph in neo4J for microbiome research Integration
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that integrates evolving knowledge graphs (KGs) with domain-specific foundation models to enhance diagnostic capabilities. This research will look into how knowledge graphs be designed and generated from
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using nanopore technologies explore structural genomic differences between populations by combining de novo assemblies into a pan-genome graph perform population genomics analyses on re-sequenced animals
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. - Apply graph-based and machine-learning tools to model signaling networks driving plasticity. - Validate candidate pathways in cell culture and mouse models. - Present findings at seminars and conferences
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and interdisciplinary data integration develop new AI-based methods, tools, scripts, ontologies and a knowledge graph based on RTG research results and relevant literature provide methodological support