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 program is funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) at the National Institutes of Health (T32HD040128-16) and is open to US citizens and permanent residents.

CHIP, an affiliate of Harvard Medical School and a collaborating program of the Harvard Medical School Department of Biomedical Informatics, is recruiting postdoctoral fellows. Founded in 1994, CHIP 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. Our faculty have been featured in The New York Times, Wall Street Journal, ABC News, CNBC, Bloomberg, CNN, Forbes, Financial Times, NBC News, GQ Magazine, U.S. News & World Report, Politico, and BBC News for their expertise on COVID-19.

We seek outstanding candidates passionate about advancing the ability to acquire and then reason over an entire spectrum of data types ranging from molecular and genomic all the way to clinical, epidemiological, environmental and social. Focus areas may include, but are not limited to research applications of machine learning/AI including COVID-19, medical applications of machine learning/AI including clinical decision support and predictive medicine, genomic and precision medicine, population health, health IT architectures and standards (e.g. SMART on FHIR apps and infrastructure), re-imagined clinical trials, real-world evidence, data visualization, and integrative omics. Candidates should have strong quantitative backgrounds.

Over the past two decades, the program has trained a mix of MDs and PhDs. More than 90 percent have gone on to receive independent funding in faculty positions in academic medicine.

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Admissions

Applications are open, and admissions are available on a rolling basis.

Eligibility

Citizens or permanent residents of the United States enrolled in a research doctoral, research postdoctoral, clinical doctoral, or clinical postdoctoral are eligible to apply. Preference will be given to candidates who have, or are seeking, board certification in pediatric emergency medicine, or who have research interests that are aligned with CHIP’s core research areas.

The program has been committed to recruiting and retaining postdoctoral trainees who are URiM. We have maintained our commitment to diversity through prioritizing applications from diverse and disadvantaged backgrounds. Women and underrepresented minority groups are strongly encouraged to apply.

How to apply

Click here to ask questions.

To apply, send a CV, cover letter, personal statement, and three letters of reference to megan.rollins@childrens.harvard.edu.

Publications

Zhang A, Teng L, Alterovitz G. An explainable machine learning platform for pyrazinamide resistance prediction and genetic feature identification of Mycobacterium tuberculosis. Journal of the American Medical Informatics Association : JAMIA 2020.

Geva A, Stedman JP, Manzi SF, Lin C, Savova GK, Avillach P, Mandl KD. Adverse drug event presentation and tracking (ADEPT): semiautomated, high throughput pharmacovigilance using real-world data. JAMIA open 2020.

Börcsök J, Sztupinszki Z, Bekele R, Gao SP, Diossy M, Samant AS, Dillon KM, Tisza V, Spisák S, Rusz O, Csabai I, Pappot H, Frazier ZJ, Konieczkowski DJ, Liu D, Vasani N, Rodrigues JA, Solit DB, Hoffman-Censits JH, Plimack ER, Rosenberg JE, Lazaro JB, Taplin ME, Iyer G, Brunak S, Lozsa R, Van Allen EM, Szüts D, Mouw KW, Szallasi Z. Identification of a synthetic lethal relationship between nucleotide excision repair (NER) deficiency and irofulven sensitivity in urothelial cancer. Clinical cancer research : an official journal of the American Association for Cancer Research 2020.

Gokuldass A, Draghi A, Papp K, Borch TH, Nielsen M, Westergaard MCW, Andersen R, Schina A, Bol KF, Chamberlain CA, Presti M, Met Ö, Harbst K, Lauss M, Soraggi S, Csabai I, Szállási Z, Jönsson G, Svane IM, Donia M. Qualitative Analysis of Tumor-Infiltrating Lymphocytes across Human Tumor Types Reveals a Higher Proportion of Bystander CD8 T Cells in Non-Melanoma Cancers Compared to Melanoma. Cancers 2020.

Perera G, Rijnbeek PR, Alexander M, Ansell D, Avillach P, Duarte-Salles T, Gordon MF, Lapi F, Mayer MA, Pasqua A, Pedersen L, van Der Lei J, Visser PJ, Stewart R. Vascular and metabolic risk factor differences prior to dementia diagnosis: a multidatabase case-control study using European electronic health records. BMJ open 2020.