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of the chemicals in biological tissues without the use of labels. MSI can help us better understand and diagnose diseases such as, for example, cancer. Most MSI is performed using ultraviolet (UV) lasers and an
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-resolution microscopy, to resolve synaptic protein complexes at high resolution in defined synapses in brain tissue. The successful candidate will be a member of the MacGillavry lab that is part of the larger
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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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to the design and application of innovative chemical strategies, including crosslinking, proximity labeling, and biocompatible photocatalysis for method development and fundamental discovery. Key tasks in the lab
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labeling, and biocompatible photocatalysis for method development and fundamental discovery. Key tasks in the lab include organic synthesis, compound characterization, biochemical assays, cell-based assays
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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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ground truth, labeling strategies, acceptance criteria, and validation experiments appropriate for low–medium TRL projects. Build dashboards/visualizations and clear reports that communicate model
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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