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algorithms to detect complex structural variants in humans using long DNA sequencing reads. A structural variant (SV) is a large-scale alteration in the genome that involves rearranged, deleted, or inserted
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the developmental rules underlying phenotypic variation. The successful postdoctoral fellow will develop and implement an empirical framework that utilizes data-driven algorithms to learn relationships between past
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within tissues using our in-house developed spatial transcriptomics-based technology (Spatial VDJ). Using established and newly developed algorithms, we map B cell evolution within tissues, including class
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at the single-cell level, using tools from optimal transport, mathematical optimization, and machine learning. In addition to method development, the work includes applying and benchmarking algorithms on both
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education . Skills and personal qualities In addition to the aforementioned requirements for the position: A demonstrable computational competence, comfortable with using and developing algorithms, data
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equivalent competence. Experience with analysis software such as ImageJ/Fiji, CellProfiler, Napari, QuPath, etc. Experience with algorithm development and development of analysis pipelines aiming to quantify
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Scientific Computing the research and education has a unique breadth, with large activities in classical scientific computing areas such as mathematical modeling, development and analysis of algorithms