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

Geisler BP, Zahabi L, Lang AE, Eastwood N, Tennant E, Lukic L, Sharon E, Chuang HH, Kang CB, Clayton-Johnson K, Aljaberi A, Yu H, Bui C, Le Mau T, Li WC, Teodorescu D, Hinske LC, Sun DL, Manian FA, Dunn AG. Repurposing existing medications for coronavirus disease 2019: protocol for a rapid and living systematic review. Systematic reviews 2021.

Börcsök J, Diossy M, Sztupinski Z, Prosz A, Tisza V, Spisak S, Rusz O, Stormoen DR, Pappot H, Csabai I, Brunak S, Mouw KW, Szallasi Z. Detection of Molecular Signatures of Homologous Recombination Deficiency in Bladder Cancer. Clinical cancer research : an official journal of the American Association for Cancer Research 2021.

Geva A, Albert BD, Hamilton S, Manning MJ, Barrett MK, Mirchandani D, Harty M, Morgan EC, Kleinman ME, Mehta NM. eSIMPLER: A Dynamic, Electronic Health Record-Integrated Checklist for Clinical Decision Support During PICU Daily Rounds. Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies 2021.

Li J, Tiwari A, Mirzakhani H, Wang AL, Kho AT, McGeachie MJ, Litonjua AA, Weiss ST, Tantisira KG. Circulating MicroRNA: Incident Asthma Prediction and Vitamin D Effect Modification. Journal of personalized medicine 2021.

Larsen RJ, Gagoski B, Morton SU, Ou Y, Vyas R, Litt J, Grant PE, Sutton BP. Quantification of magnetic resonance spectroscopy data using a combined reference: Application in typically developing infants. NMR in biomedicine 2021.