Harvard Medical School Instructor, Assistant, or Associate Professor
in the Computational Health Informatics Program
Boston Children’s Hospital
Boston, MA 

Establish your independent research program in computational health at Harvard and Boston Children’s Hospital.

CHIP, the Computational Health Informatics Program at Boston Children’s Hospital, an affiliate of Harvard Medical School and a collaborating program of its Department of Biomedical Informatics, is recruiting research faculty at junior and senior levels to join us in transforming care delivery and biomedical science.

We seek outstanding candidates passionate about advancing the ability to acquire and then reason over an entire spectrum of data types ranging from clinical, epidemiological, environmental and social, all the way down to molecular and genomic.  

Candidates expert in health IT architectures and standards (e.g. SMART on FHIR apps and infrastructure) will be given special consideration. Areas of focus can include machine learning/AI including clinical decision support and predictive medicine, precision medicine, population health, real-world evidence and data visualization. 

Candidates at the Instructor or Assistant Professor level should have strong quantitative backgrounds and a history of innovative approaches to biomedical scientific inquiry or the translation of computational methods to engineering or software applications in medicine. Candidates at the Associate Professor level must additionally demonstrate scientific leadership and the ability to develop sustainable, impactful projects.

All qualified applicants must have a doctoral degree (MD, PhD, MD/PhD, or equivalent) and a strong record of publishing. Faculty are expected to teach, be exceptional mentors and have or achieve substantial independent funding.

CHIP, founded in 1994, is a multidisciplinary applied research and education program. Biomedical informatics has become a major theme and methodology for biomedical science, health care delivery, and population health, involving high-dimensional modeling and understanding of patients from the molecular to the population levels. We design information infrastructure for medical decision making, diagnosis, care redesign, public health management, and re-imagined clinical trials. The field is inherently interdisciplinary, drawing on traditional biomedical disciplines, the science and technology of computing, data science, biostatistics, epidemiology, decision theory, omics, implementation science, and health care policy and management. Our faculty are trained in medicine, data science, computer science, mathematics and epidemiology. Though CHIP has a robust pediatric research agenda, our interests span across all ages. CHIP research highlights are here.

For the work of CHIP, Health 2.0 voted Boston Children’s Hospital the 10 Year Global Retrospective Top Influencer among all health care organizations.

Boston Children’s Hospital is an equal opportunity employer that strongly encourages women and underrepresented minorities candidates apply for positions at our institution.

Interested applicants should submit a CV, cover letter, research plan, three references and three letters of support to Kenneth D. Mandl, MD, MPH, CHIP Director by emailing them to Megan Rollins (megan.rollins@childrens.harvard.edu).

Applications may be considered on a rolling basis so there could be an advantage to submitting early.

 

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.