A repository for the NIH Deep Learning Scientific Interest Group (DL SIG)
Machine learning techniques promise to revolutionize our ability to analyze the enormous quantities of data generated in the course of biomedical research. Labs at the NIH are working to develop the software tools to realize that promise, and we are looking for students with strong math, computer science, and data science backgrounds to help us build those tools.
Multiple groups at the National Institutes of Health are interested in hiring undergraduate and graduate student summer interns for machine learning projects as part of the NIH Summer Internship Program (SIP).
Bethesda, MD
Negotiable. 8-10 weeks between mid-May and and August.
The NIH SIP online application does not allow people to apply to individual labs directly. Instead, applicants are encouraged to contact the labs they wish to work with by email. Below is an outline of some labs interested in pursuing machine learning projects this summer, as well as contact information.
Led by Dr. Ronald Summers (rms@nih.gov), the Imaging Biomarkers and Computer-Aided Diagnosis Laboratory has a long-standing interest in developing machine learning techniques to improve computer-aided diagnosis (CAD) of disease from radiological images.
Dr. Kedar Narayan (narayank@mail.nih.gov) is the group leader for cellular imaging at the Center for Molecular Microscopy. His team is interested in developing computer vision algorithms for high-resolution imaging of biological structures using electron microscopy (FIB-SEM).
Led by Richard Leapman, the Laboratory of Cellular Imaging and Macromolecular Biophysics has an ongoing project headed by Matthew Guay (matthew.guay@nih.gov) to develop effective automated segmentation algorithms for analyzing large-scale tissue structure using electron microscopy (SBF-SEM).
Led by Justin Taraska (justin.taraska@nih.gov), the Laboratory of Molecular and Cellular Imaging develops and uses advanced fluorescence and electron microscopy methods as well as biochemical, cellular, and biophysical tools to understand the nanoscale architecture of the proteins that regulate key cellular events. His team is interested in building new software tools to improve and accelerate analysis of the lab’s cellular image data.
Led by Mark Histed (mark.histed@nih.gov), the Unit on Neural Computation and Behavior is looking for a PhD student interested in using principles from deep learning to understand the brain, using optical 2-photon experiments in behaving mice.
Dr. George Thoma (gthoma@mail.nih.gov) is the Chief of the Communications Engineering Branch of the Lister Hill National Center for Biomedical Communications, a division of the National Library of Medicine. Dr. Thoma oversees multiple image processing and recognition projects, including research on automated research article analysis, as well as low-cost malaria detection tools using machine learning software deployed on smartphones.
Individual project duties and requirements vary, but common items are as follows.
Please contact labs directly for lab-specific details.
Interested individuals should make sure they meet the NIH SIP eligibility criteria. Please note that the program is open only to US citizens and permanent residents, age 16 or older as of June 15, 2018, enrolled at least half-time in an accredited university or planning to be enrolled in the fall of 2018.
One of the goals of the NIH is to build a diverse and inclusive scientific workforce. Therefore, we encourage students from underrepresented communities or disadvantaged backgrounds to apply for the NIH SIP.
All applications are due by March 1, 2018
Remember - since the SIP application procedure does not let you specify a particular lab, you should contact labs directly to indicate your interest in working on a project.
Please direct general questions about the application procedure to Matthew Guay at matthew.guay@nih.gov.
For questions about lab-specific projects, please email the provided point of contact for the lab.