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”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case
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within a species, going beyond the limitations of single-reference genomes. By integrating multiple genomes from different individuals or populations, pangenomes can provide a more comprehensive
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