Postdoctoral Fellow - Deep Learning and Computational Pathology

A research program co-led by Drs. Eytan Ruppin (Cancer Data Science Lab, https://ccr.cancer.gov/staff-directory/eytan-ruppin) and Kenneth Aldape (Lab of Pathology, https://ccr.cancer.gov/staff-directory/kenneth-aldape) at the NCI is inviting applications for a Postdoctoral Research Fellowship position in the field of computational pathology using supervised, weakly supervised, and unsupervised machine-learning/deep learning algorithms and methods with applications in cancer biology and diagnosis. The Postdoctoral Fellow would aim to identify clinically relevant genomic alterations and to improve clinical practice in diagnostic pathology by developing deep learning tools that lead to more objective and reproducible results, with an eye towards broad applications, including in resource-limited and developing regions. The overarching goal is to lead in the transformation of diagnostic cancer pathology from a qualitative to a quantitative discipline. The Postdoctoral Fellow will join a dynamic team of cancer data scientists and pathologists and participate in unique opportunities to apply deep learning for important scientific breakthroughs, precision diagnostics and treatment, to directly impact patient care at a global level (see, e.g. https://www.biorxiv.org/content/10.1101/2022.06.07.495219v3.abstract).

This position will be within the joint labs of Eytan Ruppin and Kenneth Aldape at the National Cancer Institute in Bethesda, Maryland. The candidate will have the opportunity to work in a team of expert deep learning fellows with substantial data and computational resources, including digital pathology archives, a robust HPC cluster and internal servers.

The successful candidate will have a PhD, MD, or MD/PhD with experience in deep learning and machine learning. A track record of publication in computational biology and /or machine learning (preferably deep learning). The NCI is an exciting and collaborative research environment dedicated to achieving substantive breakthroughs in cancer biology and precision oncology.

The successful candidate will have a PhD, MD, or MD/PhD with experience in deep learning and machine learning. A track record of publication in computational biology and /or machine learning (preferably deep learning).

The NIH is dedicated to building a community in its training and employment programs and encourages the application and nomination of qualified women, minorities, and individuals with disabilities.