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

Gao Y, Dligach D, Miller T, Tesch S, Laffin R, Churpek MM, Afshar M. Hierarchical Annotation for Building A Suite of Clinical Natural Language Processing Tasks: Progress Note Understanding. LREC ... International Conference on Language Resources & Evaluation : [proceedings]. International Conference on Language Resources & Evaluation 2022.

Valtchinov VI, Murphy SN, Lacson R, Ikonomov N, Zhai BK, Andriole K, Rousseau J, Hanson D, Kohane IS, Khorasani R. Analytics to monitor local impact of the Protecting Access to Medicare Act's imaging clinical decision support requirements. Journal of the American Medical Informatics Association : JAMIA 2022.

Gao Y, Dligach D, Christensen L, Tesch S, Laffin R, Xu D, Miller T, Uzuner O, Churpek MM, Afshar M. A scoping review of publicly available language tasks in clinical natural language processing. Journal of the American Medical Informatics Association : JAMIA 2022.