Postdoctoral Training

There is a common core of knowledge, skills, and experiences to engage meaningfully in the field of informatics. CHIP offers training at many levels and is well integrated with the vibrant academic community at the Harvard Department for Biomedical Informatics and affiliated hospital-based training programs, providing fellows with many opportunities for interaction and collaboration.

The Computational Health Informatics Program (CHIP) at Boston Children’s Hospital hosts a training program for postdoctoral fellows to be trained in Informatics, Genomics, Machine Learning, Artificial Intelligence, and Biomedical Data Science.

The Health Natural Language Processing Lab at Boston Children’s Hospital is seeking a post-doctoral research fellow to contribute to cutting edge research in the field of health natural language processing.

The Clarity- and Virtue-guided Algorithms (CAVA) Lab at Boston Children's Hospital / Harvard Medical School is seeking a post-doctoral research fellow to advance the interpretability and fairness of machine learning (ML) models deployed in critical healthcare settings.

The Lee Lab in the Vascular Biology Program at Boston Children’s Hospital invites applications for a postdoctoral fellow position in the field of artificial intelligence-based analysis of biomedical spatiotemporal data.

Internships

The Boston Children's Hospital Computational Health Informatics Program (CHIP) is Harvard Medical School affiliated, multidisciplinary applied research and education program. CHIP is uniquely positioned at the nexus of a world-leading children’s hospital, a first-rate academic institution, wider health networks, and thoughtful collaborations with industry.

Our research has been at the forefront of posing a wide spectrum of health questions and building solutions. Our faculty advance the science of biomedical informatics for molecular characterization of the patient, gene discovery, medical decision making, diagnosis, therapeutic selection, care redesign, public health management, population health, and re-imagined clinical trials. Our research has influenced public health policies at the highest level. Governmental health institutions, like the Centers for Disease Control and Prevention, have amended their recommendations for population health based on the research of our faculty. Our faculty have advised governments worldwide on establishing biodefense and biosurveillance infrastructures. The White House, US State Department, USAID, and NASA have recognized our faculty for their research contributions in health care.

CHIP has a longstanding track record of developing and repurposing existing technologies that have been widely commercialized. We have established partnerships with  companies like Uber, Lyft, Quest Diagnostics, and Eli Lily and have developed platforms that have been widely adopted by Apple, Google, Microsoft, and Amazon.

The CHIP AI Internship is an opportunity for undergraduate students at Harvard College to develop new machine learning and artificial intelligence approaches and apply them to fundamental challenges in biomedicine.

Project Advocacy Interns will support the team in promoting projects, social media, compiling reports, working on event logistics, and pulling campaign metrics.

Publications

Hswen Y, Thorpe Huerta D, Le-Compte C, Hawkins JB, Brownstein JS. A 10-Year Social Media Analysis Exploring Hospital Online Support of Black Lives Matter and the Black Community. JAMA network open 2021.

Le TT, Gutiérrez-Sacristán A, Son J, Hong C, South AM, Beaulieu-Jones BK, Loh NHW, Luo Y, Morris M, Ngiam KY, Patel LP, Samayamuthu MJ, Schriver E, Tan ALM, Moore J, Cai T, Omenn GS, Avillach P, Kohane IS, , Visweswaran S, Mowery DL, Xia Z. Multinational characterization of neurological phenotypes in patients hospitalized with COVID-19. Scientific reports 2021.

Steffens MS, Dunn AG, Marques MD, Danchin M, Witteman HO, Leask J. Addressing Myths and Vaccine Hesitancy: A Randomized Trial. Pediatrics 2021.

Mohsen H, Gunasekharan V, Qing T, Seay M, Surovtseva Y, Negahban S, Szallasi Z, Pusztai L, Gerstein MB. Network propagation-based prioritization of long tail genes in 17 cancer types. Genome biology 2021.

Zhang HG, Hejblum BP, Weber GM, Palmer NP, Churchill SE, Szolovits P, Murphy SN, Liao KP, Kohane IS, Cai T. ATLAS: an automated association test using probabilistically linked health records with application to genetic studies. Journal of the American Medical Informatics Association : JAMIA 2021.