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integrating background knowledge and weak supervision, e.g. based on labels in downstream tasks, which can be for example logical constraints. As part of the project, you will ideally also perform a 6 month to
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. Account for reference data uncertainty (e.g. timing uncertainty and labelling errors) and incorporate it into performance metrics such as accuracy, timeliness, and detection delay. Design a spatiotemporal
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-cell and bulk transcriptomics, mutant screens and transformation approaches to identify signalling pathways and regulatory components. You will analyze data and integrate multi-modal datasets by applying
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workflows; some experience with AI and machine learning methods to label texts (NLP) or data sources; strong programming skills (e.g., Python, RDF) and some skills in web-based data retrieval; good English