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.

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

Morales A, Ing A, Antolik C, Austin-Tse C, Baudhuin LM, Bronicki L, Cirino A, Hawley MH, Fietz M, Garcia J, Ho C, Ingles J, Jarinova O, Johnston T, Kelly MA, Kurtz CL, Lebo M, Macaya D, Mahanta L, Maleszewski J, Manrai AK, Murray M, Richard G, Semsarian C, Thomson KL, Winder T, Ware JS, Hershberger RE, Funke BH, Vatta M, . Harmonizing the Collection of Clinical Data on Genetic Testing Requisition Forms to Enhance Variant Interpretation in Hypertrophic Cardiomyopathy (HCM): A Study from the ClinGen Cardiomyopathy Variant Curation Expert Panel. The Journal of molecular diagnostics : JMD 2021.

Kohane IS, Aronow BJ, Avillach P, Beaulieu-Jones BK, Bellazzi R, Bradford RL, Brat GA, Cannataro M, Cimino JJ, García-Barrio N, Gehlenborg N, Ghassemi M, Gutiérrez-Sacristán A, Hanauer DA, Holmes JH, Hong C, Klann JG, Loh NHW, Luo Y, Mandl KD, Mohamad D, Moore JH, Murphy SN, Neuraz A, Ngiam KY, Omenn GS, Palmer N, Patel LP, Pedrera-Jiménez M, Sliz P, South AM, Tan ALM, Taylor DM, Taylor BW, Torti C, Vallejos AK, Wagholikar KB, Weber GM, Cai T. What Every Reader Should Know About Studies Using Electronic Health Record Data but May be Afraid to Ask. Journal of medical Internet research 2021.

Cutillo CM, Sharma KR, Foschini L, Kundu S, Mackintosh M, Mandl KD, . Machine intelligence in healthcare-perspectives on trustworthiness, explainability, usability, and transparency. NPJ digital medicine 2020.

Brat GA, Weber GM, Gehlenborg N, Avillach P, Palmer NP, Chiovato L, Cimino J, Waitman LR, Omenn GS, Malovini A, Moore JH, Beaulieu-Jones BK, Tibollo V, Murphy SN, Yi SL, Keller MS, Bellazzi R, Hanauer DA, Serret-Larmande A, Gutierrez-Sacristan A, Holmes JJ, Bell DS, Mandl KD, Follett RW, Klann JG, Murad DA, Scudeller L, Bucalo M, Kirchoff K, Craig J, Obeid J, Jouhet V, Griffier R, Cossin S, Moal B, Patel LP, Bellasi A, Prokosch HU, Kraska D, Sliz P, Tan ALM, Ngiam KY, Zambelli A, Mowery DL, Schiver E, Devkota B, Bradford RL, Daniar M, Daniel C, Benoit V, Bey R, Paris N, Serre P, Orlova N, Dubiel J, Hilka M, Jannot AS, Breant S, Leblanc J, Griffon N, Burgun A, Bernaux M, Sandrin A, Salamanca E, Cormont S, Ganslandt T, Gradinger T, Champ J, Boeker M, Martel P, Esteve L, Gramfort A, Grisel O, Leprovost D, Moreau T, Varoquaux G, Vie JJ, Wassermann D, Mensch A, Caucheteux C, Haverkamp C, Lemaitre G, Bosari S, Krantz ID, South A, Cai T, Kohane IS. International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium. NPJ digital medicine 2020.