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Field
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affordance-level understanding of the environment over time. Emphasis will be placed on dynamically adapting the fusion process to modality confidence, constructing rich scene graphs that encode geometry
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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
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to surveillance of infectious pathogens using computer science and mathematics? Join the Delft Bioinformatics Lab and work on graph-based algorithms for microbial genomics! Job description Bacterial and viral
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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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Do you want to contribute to surveillance of infectious pathogens using computer science and mathematics? Join the Delft Bioinformatics Lab and work on graph-based algorithms for microbial genomics
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technologies, such as ontologies and semantic web standards, as well as graph data and knowledge graphs, and circular economy and DPPs is preferential. Documented experience of writing and publishing research
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in writing scientific papers based on research findings. This includes generating graphs, tables, and other visuals for internal reports, grant applications, and publications. Collaborates with other
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on the following tasks with either with a stronger model-development or application focus: Design knowledge-graph-augmented transformers and retrieval-augmented generation (RAG) pipelines that enable
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with data visualization (graphs, GIS maps, figures). Proficiency with GIS software and spatial data analysis is highly desirable. Strong writing skills, including the ability to contribute to manuscripts