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- Delft University of Technology (TU Delft); yesterday published
- Delft University of Technology (TU Delft)
- Leiden University
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- Delft University of Technology (TU Delft); 17 Oct ’25 published
- Eindhoven University of Technology (TU/e)
- Eindhoven University of Technology (TU/e); Eindhoven
- Leiden University; Leiden
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Field
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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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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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embedding graph-based problems, particularly those known to be challenging for classical computing architectures. Some of your responsibilities will include: Design and develop mixed-signal circuits
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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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, 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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programs. Alternatively, Mathematics, Computer Science, Computer Engineering, Electrical Engineering, or a similar field; Strong mathematical background: basic knowledge of graph theory and excellent
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a similar field; Strong mathematical background: basic knowledge of graph theory and excellent background in linear algebra, finite fields and rings; Strong background in digital hardware design and
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, or a similar field; Strong mathematical background: basic knowledge of graph theory and excellent background in linear algebra, finite fields and rings; Strong background in digital hardware design and
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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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embedding graph-based problems, particularly those known to be challenging for classical computing architectures. Some of your responsibilities will include: Design and develop mixed-signal circuits