The Newman Lab in the Department of Biomedical Data Science at Stanford University is seeking a postdoctoral fellow with a strong background in single cell genomics, biostatistics, and biomedical data science to develop novel computational methods for a variety of imputation problems in the analysis of single cell sequencing data from cancer patients. Applications should have strong familiarity with statistical programming languages (R, Python), and experience with neural networks and TensorFlow is a plus. Demonstrated ability in the field is important and applicants should have at least 1 or 2 first author publications in a related area, showcasing their creativity and potential. A track record of conference presentations is also expected. Successful applicants will have ample opportunities to work with rich genomic data sets from a variety of cancer types and to work closely with basic and clinical science collaborators, both at Stanford and elsewhere.