Genomes encode instructions for cells to regulate gene activity in response to their environment. Gene regulation consists of two major steps. First, genes are transcribed into mRNA. Second, post-transcriptional mechanisms regulate mRNA stability and the rate at which it is translated into proteins. This second step of gene regulation is still poorly understood because relevant parameters such as mRNA half-life, mRNA protein binding and subcellular localization are difficult to assay. In this project we will exploit the advantages of the model eukaryote Saccharomyces cerevisiae and other species covering a broad evolutionary range to derive the first comprehensive sequence-based model of eukaryotic gene regulation. We combine innovative high-throughput technologies to probe post-transcriptional regulation at an unprecedented scale across a broad range of species and conditions with synthetic biology to massively test regulatory sequences. Deep learning on these data allows us to build predictive models and unravel complex regulatory instructions. Ultimately, these insights will not only allow us to better understand the regulatory code, but also to design new regulatory modules tailored to specific applications. This application is related to the ERC Synergy project EPIC (https://ercsynergy-epic.eu/en)