Postdoctoral Fellow - Machine Learning-Enabled Pathology

Ongoing until position is filled.



The Department of Biomedical Informatics (DBMI) at Harvard Medical School and the Yu Lab are seeking a Postdoctoral Fellow with experience in machine learning and scientific programming. The candidate will work with a multi-disciplinary team of researchers, including bioinformaticians, pathologists, oncologists, and computer scientists, and conduct original research on computational pathology.

Digital pathology images contain rich information on complex diseases. The goal of our efforts is to build and apply automated analytical pipelines for various types of pathology data, including histopathology images and multi-omics (e.g., genomics, epigenomics, transcriptomics, and proteomics) information.


A Ph.D. in a Biomedical Informatics, Computational Biology, Computer Science, or a related field is required. Substantial experience in machine learning, Python and R programming, and familiarity with deep learning packages (e.g., TensorFlow, Keras, or PyTorch) is essential. Strong problem-solving and communication skills are highly desirable for this opportunity.


The postdoctoral fellow will develop and refine machine learning methods for analyzing histopathology, clinical, and multi-omics data, collaborate with our multi-disciplinary team, and publish the findings.

Start date

This position is available immediately and can be renewed annually.

How to Apply

Please send your curriculum vitae, two representative papers, a brief research statement, and the names and contact information of three references to Kun-Hsing Yu, MD, PhD.(<>)