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engineering starts from use cases (typical and exceptional) and various system scenarios (different operating modes, failures). This will require the development of suitable domain-specific languages (DSLs
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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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, 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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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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optimizations tailored to different environments. The optimizations range from algebraic optimizations (e.g., term rewriting) to algorithmic optimizations (e.g., group level algorithms), and to hardware
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graphs for heterogeneous pavement engineering knowledge aiming to speed up the learning cycle and support innovation and asset management. Job description The increasing accessibility of data in
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work will focus on identifying the mathematical knowledge and properties to guide hardware optimizations tailored to different environments. The optimizations range from algebraic optimizations (e.g
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. Your work will focus on identifying the mathematical knowledge and properties to guide hardware optimizations tailored to different environments. The optimizations range from algebraic optimizations (e.g