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. Internships will be on a rolling basis and every intern will be matched with a core CHIP faculty member.

Interns will be exposed to the many facets of artificial intelligence and machine learning applied to challenges in healthcare, including:

  • the analysis of very large datasets spanning tens of millions individuals
  • digital surveillance and machine learning approaches for public health
  • clinical decision making using high-throughput molecular and clinical data (e.g. whole-exome sequencing)
Admissions

A call for applications is now open and positions are available on a rolling basis.

Qualifications

Being an intern at CHIP means being given meaningful, fulfilling, skill-building tasks and projects that are designed to set you up for success in your future career. Interns are/have:

  • current undergraduate students at Harvard College
  • strong quantitative and computer science skills (relevant [but not required] coursework: CS50, Stat 110/111, CS181, CS109, BMI704)
  • hardworking, detail-oriented, and efficient
  • an interest in machine learning in health care
  • an ability to multitask, work independently, and be self-directed
How to apply

Please send your resume, cover letter, and 2 letters of reference from professors who know your work to chip@childrens.harvard.edu and reference "CHIP AI Internship" in the subject line.

Publications

Levy S, Wisk LE, Chadi N, Lunstead J, Shrier LA, Weitzman ER. Validation of a single question for the assessment of past three-month alcohol consumption among adolescents. Drug and alcohol dependence 2021.

Wu F, Xiao A, Zhang J, Moniz K, Endo N, Armas F, Bonneau R, Brown MA, Bushman M, Chai PR, Duvallet C, Erickson TB, Foppe K, Ghaeli N, Gu X, Hanage WP, Huang KH, Lee WL, Matus M, McElroy KA, Nagler J, Rhode SF, Santillana M, Tucker JA, Wuertz S, Zhao S, Thompson J, Alm EJ. SARS-CoV-2 RNA concentrations in wastewater foreshadow dynamics and clinical presentation of new COVID-19 cases. The Science of the total environment 2021.

Weber GM, Zhang HG, L'Yi S, Bonzel CL, Hong C, Avillach P, Gutiérrez-Sacristán A, Palmer NP, Tan AL, Wang X, Yuan W, Gehlenborg N, Alloni A, Amendola DF, Bellasi A, Bellazzi R, Beraghi M, Bucalo M, Chiovato L, Cho K, Dagliati A, Estiri H, Follett RW, García-Barrio N, Hanauer DA, Henderson DW, Ho YL, Holmes JH, Hutch MR, Kavuluru R, Kirchoff K, Klann JG, Krishnamurthy AK, Le TT, Liu M, Loh NHW, Lozano-Zahonero S, Luo Y, Maidlow S, Makoudjou A, Malovini A, Moal B, Morris M, Mowery DL, Murphy SN, Neuraz A, Ngiam KY, Okoshi MP, Omenn GS, Patel LP, Pedrera-Jiménez M, Prudente RA, Samayamuthu MJ, Sanz J, Schriver ER, Schubert P, Serrano-Balazote P, Tan BW, Tanni SE, Tibollo V, Visweswaran S, Wagholikar KB, Xia Z, Zöller D, Kohane IS, Cai T, South AM, Brat GA. International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: a 4CE Consortium Study. Journal of medical Internet research 2021.

Mandl KD, Perakslis ED. HIPAA and the Leak of "Deidentified" EHR Data. Reply. The New England journal of medicine 2021.

Rees CA, Monuteaux MC, Herdell V, Fleegler EW, Bourgeois FT. Correlation Between National Institutes of Health Funding for Pediatric Research and Pediatric Disease Burden in the US. JAMA pediatrics 2021.