Plants control the fine-grained expression of thousands of genes during growth, development, or in response to external stimuli. While gene expression varies strongly between different organs and tissues, how this specificity is encoded in the genome is largely unknown. The aim of this project is uniting single-cell genomics, explainable artificial intelligence (xAI), and synthetic promoter engineering, to efficiently learn and validate regulatory sequences controlling gene expression in plants. Based on high-resolution single-cell gene expression profiling in the model Arabidopsis, xAI models will be built predicting gene expression under control and stress conditions while at the same time identifying the underlying regulatory DNA sequences and syntax. By combining the power of advanced computational and experimental methods, I will learn novel regulatory sequences and design novel synthetic promoters controlling gene expression in complex cellular contexts.