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

Hopper RK, Abman SH, Elia EG, Avitabile CM, Yung D, Mullen MP, Austin ED, Bates A, Handler SS, Feinstein JA, Ivy DD, Kinsella JP, Mandl KD, Raj JU, Sleeper LA, . Pulmonary Hypertension in Children with Down Syndrome: Results from the Pediatric Pulmonary Hypertension Network Registry. The Journal of pediatrics 2022.

Wang X, Zhang HG, Xiong X, Hong C, Weber GM, Brat GA, Bonzel CL, Luo Y, Duan R, Palmer NP, Hutch MR, Gutiérrez-Sacristán A, Bellazzi R, Chiovato L, Cho K, Dagliati A, Estiri H, García-Barrio N, Griffier R, Hanauer DA, Ho YL, Holmes JH, Keller MS, Klann MEng JG, L'Yi S, Lozano-Zahonero S, Maidlow SE, Makoudjou A, Malovini A, Moal B, Moore JH, Morris M, Mowery DL, Murphy SN, Neuraz A, Yuan Ngiam K, Omenn GS, Patel LP, Pedrera-Jiménez M, Prunotto A, Jebathilagam Samayamuthu M, Sanz Vidorreta FJ, Schriver ER, Schubert P, Serrano-Balazote P, South AM, Tan ALM, Tan BWL, Tibollo V, Tippmann P, Visweswaran S, Xia Z, Yuan W, Zöller D, Kohane IS, Avillach P, Guo Z, Cai T, . SurvMaximin: Robust federated approach to transporting survival risk prediction models. Journal of biomedical informatics 2022.

Torous J, Stern AD, Bourgeois FT. Regulatory considerations to keep pace with innovation in digital health products. NPJ digital medicine 2022.

Levy S, Wisk LE, Minegishi M, Ertman B, Lunstead J, Brogna M, Weitzman ER. Association of Screening and Brief Intervention With Substance Use in Massachusetts Middle and High Schools. JAMA network open 2022.

Nielsen M, Presti M, Sztupinszki Z, Jensen AWP, Draghi A, Chamberlain CA, Schina A, Yde CW, Wojcik J, Szallasi Z, Crowther MD, Svane IM, Donia M. Co-existing alterations of MHC class I antigen presentation and IFNγ signaling mediate acquired resistance of melanoma to post-PD-1 immunotherapy. Cancer immunology research 2022.