Project to be confirmed after rotation year

推断最近的适应如何塑造基因组结构对于理解支撑物种多样性和种群内遗传变异的分子机制至关重要。最近人工智能的兴起为这一领域创造了新的机会。基于深度学习的新方法显示出巨大的前景，特别是在现有方法缺乏能力的情况下。该项目将以该领域的最新进展为基础，进一步开发深度学习算法，从大规模基因组数据中推断适应性进化，并在野生种群数据和受控的黑腹果蝇进化和序列实验中对其进行测试。

Inferring how recent adaptation shaped genomic architecture is crucial tounderstanding the molecular mechanisms underpinning diversity among species andgenetic variation within populations. The recent rise of artificial intelligence hascreated new opportunities in this area. Novel approaches based on deep learning areshowing great promise, especially for scenarios where existing methods are lackingpower. This project will build on recent advances in the field to further develop deeplearning algorithms to infer adaptive evolution from large-scale genomic data and testthem on data from wild populations and controlled evolve-and-resequenceexperiments in Drosophila melanogaster.