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are regulated and the extent that microbial metabolism varies over time and space in relation to environmental conditions. Main responsibilities Research will involve analysis of meta-omic data from samples
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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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like computational complexity of algorithms. It’s also fairly common that we need to drill down into the code for some tool to figure out what’s wrong, so being able to read and understand code is
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these transcripts into protein sequence databases. Guide the development of proteogenomics through implementation of novel algorithms and computational analysis infrastructure Development of tools to support clinical
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to the advancement of precision medicine in oncology. A typical workday may involve writing and running code to pre-process sequencing data on a compute server, applying statistical models and algorithms to construct
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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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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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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