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department is available at: https://www.umu.se/en/department-of-computing-science/ Project description Graph transformation is a well-established theory that studies computational methods to transform graphs
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
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these technologies can only read DNA fragments of limited length. We enable biological interpretation of these sequencing data sets by developing algorithms based on graph theory, discrete optimization and machine
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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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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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because explainability is vital in health and medicine. Moreover, it leverages preferring simpler theories over complex ones if both give comparable levels of accuracy. Furthermore, it leverages the power
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previous experience with e-graphs, or is familiar with theory and algorithms used by, for example, proof assistants, term rewriting systems, optimizing compilers, program analysis tools, constraint solvers