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

Chen KY, Borglund EM, Postema EC, Dunn AG, Bourgeois FT. Reporting of clinical trial safety results in ClinicalTrials.gov for FDA-approved drugs: A cross-sectional analysis. Clinical trials (London, England) 2022.

Le Naour J, Sztupinszki Z, Carbonnier V, Casiraghi O, Marty V, Galluzzi L, Szallasi Z, Kroemer G, Vacchelli E. A loss-of-function polymorphism in compromises therapeutic outcome in head and neck carcinoma patients. Oncoimmunology 2022.

Klann JG, Strasser ZH, Hutch MR, Kennedy CJ, Marwaha JS, Morris M, Samayamuthu MJ, Pfaff AC, Estiri H, South AM, Weber GM, Yuan W, Avillach P, Wagholikar KB, Luo Y, Omenn GS, Visweswaran S, Holmes JH, Xia Z, Brat GA, Murphy SN. Distinguishing Admissions Specifically for COVID-19 from Incidental SARS-CoV-2 Admissions: A National Retrospective EHR Study. Journal of medical Internet research 2022.