In Single molecule localization microscopy (SMLM), super-resolution images of biological structures are assembled from a large number of individually detected spots. Deep neural networks (DNNs) are well suited for the task of detecting and localizing patterns in images, but for this application, and many other similar tasks in modern microscopy, no ground truth data is available for straight forward network training. Together with scientists from the HHMI Janelia Research Campus and the EMBL Heidelberg, we develop methods to train DNNs on simulations that closely resemble real data. Networks trained in this way achieve superior performance in difficult conditions and allow for much faster imaging. For more information read the article in the ML for Science Blog and university press release.