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Field
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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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for graphs; 4. Practical experience in the analysis of scientific data; 5. Proficiency in programming with Python; 6. Familiarity with the drug discovery process; 7. Ability to work on interdisciplinary
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of convolutional neural networks, graph neural networks, and attention-based architectures, with the attention mechanisms explicitly guided by the physical principles and intrinsic properties of the atmosphere
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methods you develop before being transferred to a knowledge graph-based metadata platform that you will help develop. In collaboration with stakeholders from energy research, you will develop methods
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interest in expanding their knowledge in both domains. (1) Geometry/Topology -related methods in computer science. (2) Machine Learning. (For example, graph neural networks, generative networks, or neural
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: algorithmics, graph transformation and algorithm engineering. Exposure to systems chemistry or systems biology is an asset but not a must. Proven competences in programming and ease with formal thinking are a
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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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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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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