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- Delft University of Technology (TU Delft)
- Delft University of Technology (TU Delft); yesterday published
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- Delft University of Technology (TU Delft); 17 Oct ’25 published
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families (e.g., generative models or graph/equivariant neural networks) to accelerate candidate discovery and hypothesis generation. Disseminate research findings through publications, conference
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of data handling, version control (e.g., Git), and reproducible scientific programming (desirable). Understanding of molecular representations (e.g., fingerprints, SMILES, graphs) and/or computational
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spectral graph theory. The PhD will be supervised by Anurag Bishnoi.You will have the opportunity to collaborate with Postdocs, PhD candidates, and other faculty members of the research group. You will also
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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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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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, 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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-analytical workflows, 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
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programs. Alternatively, Mathematics, Computer Science, Computer Engineering, Electrical Engineering, or a similar field; Strong mathematical background: basic knowledge of graph theory and excellent