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The Quantitative Genetics research group is interested in developing statistical genomics toolboxes to decipher the genetic architecture of important crop traits, such as grain yield, adaptation
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innovative machine learning architectures for the mining, prediction, and design of enzymes. Combine state-of-the-art ML (e.g., deep learning, generative models) with computational biochemistry tools
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independently in 10 beetle families, with a single transition to eusociality (the ambrosia beetle, Austroplatypus incompertus). The aim of this project is to uncover the detailed regulatory architecture
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