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 ‘dot’ Administration ‘at’ childrens.harvard.edu and reference "CHIP AI Internship" in the subject line.

Publications

Keloth VK, Banda JM, Gurley M, Heider PM, Kennedy G, Liu H, Liu F, Miller T, Natarajan K, V Patterson O, Peng Y, Raja K, Reeves RM, Rouhizadeh M, Shi J, Wang X, Wang Y, Wei WQ, Williams AE, Zhang R, Belenkaya R, Reich C, Blacketer C, Ryan P, Hripcsak G, Elhadad N, Xu H. Representing and Utilizing Clinical Textual Data for Real World Studies: An OHDSI Approach. Journal of biomedical informatics 2023.

Toce MS, Michelson KA, Hudgins JD, Olson KL, Monuteaux MC, Bourgeois FT. Association of prescription drug monitoring programs with benzodiazepine prescription dispensation and overdose in adolescents and young adults. Clinical toxicology (Philadelphia, Pa.) 2023.

Brown T, de Salazar Munoz PM, Bhatia A, Bunda B, Williams EK, Bor D, Miller JS, Mohareb A, Thierauf J, Yang W, Villalba J, Naranbai V, Garcia Beltran W, Miller TE, Kress D, Stelljes K, Johnson K, Larremore D, Lennerz J, Iafrate AJ, Balsari S, Buckee C, Grad Y. Geographically skewed recruitment and COVID-19 seroprevalence estimates: a cross-sectional serosurveillance study and mathematical modelling analysis. BMJ open 2023.

El-Hayek C, Barzegar S, Faux N, Doyle K, Pillai P, Mutch SJ, Vaisey A, Ward R, Sanci L, Dunn AG, Hellard ME, Hocking JS, Verspoor K, Boyle DI. An evaluation of existing text de-identification tools for use with patient progress notes from Australian general practice. International journal of medical informatics 2023.

Patik I, Redhu NS, Eran A, Bao B, Nandy A, Tang Y, El Sayed S, Shen Z, Glickman J, Fox JG, Snapper SB, Horwitz BH. The IL-10 receptor inhibits cell extrinsic signals necessary for STAT1-dependent macrophage accumulation during colitis. Mucosal immunology 2023.