Emerging Diagnostic and Investigative Technologies; EDIT ML High School Internship

EDIT AI High School Summer Internship Program

The Emerging Diagnostic and Investigative Technologies (EDIT) AI program is a unique, national-level high school internship that brings together Dartmouth Health, the Dartmouth Cancer Center’s CRTEC initiative, and Cedars Sinai Medical Center. Since its launch in 2019, EDIT AI has been dedicated to training the next generation of biomedical researchers by engaging high school students in hands‑on research at the intersection of artificial intelligence (AI) and medicine.

Throughout the program, interns gain access to powerful resources including a Virtual Student Laboratory (high-performance computing environment), a series of preparatory workshops, structured multiphase mentorship, and regular seminars led by experts in the field. Interns work on cutting‑edge research, developing computational methods to analyze and integrate diverse biomedical data types, such as whole slide pathology images, text-based pathology reports, metagenomic sequences, graphs, electronic health records, and multi‑omics datasets.

EDIT AI offers a highly collaborative environment where students can learn to handle complex, high‑dimensional data using programming languages like R and Python as well as advanced computational tools. Interns also have the chance to present their findings to clinical research audiences, hone their scientific communication skills, and contribute to potential research publications.

Through interactive lectures, hands‑on guided projects, and IRB‑supported research studies, students design and apply algorithms to real‑world pathology problems—including cancer detection, gigapixel image processing, biomedical text prediction, and more. We seek passionate high school interns with a strong foundation in computer science, some prior exposure to machine learning/deep learning concepts, and a motivation to use computation for public good.

Over 100 projects have been completed by EDIT AI summer interns—see a selection of these exciting projects here:
Projects .

If you have any follow up questions, please feel free to reach out by email to:

• Please cc' Dr. Louis Vaickus at louis.j.vaickus and Dr. Joshua Levy at joshua.j.levy or joshua.levy.

Please find more information about the program here .

If you are interested in this opportunity, please fill out an internship application form below:

• 3/1: latest day to submit an application.
• 2/20-4/22: interviews are conducted.
• 3/27:
  Early applicant decisions
• 4/15-6/15:
  Onboarding

The Levy Lab offers research and training opportunities for undergraduate, master’s, and doctoral students interested in computational biology, digital pathology, spatial transcriptomics, and clinical data science. The lab is based at Cedars‑Sinai Medical Center (CSMC), with continued research mentorship at Dartmouth Health through Dr. Joshua Levy’s adjunct appointment.

The Levy Lab develops, validates, and deploys computational and artificial intelligence methods for translational and clinical research, with a primary emphasis on spatial transcriptomics, digital pathology, multi‑omics informatics, and electronic health record‑linked analytics. Research areas include spatial transcriptomics assays (e.g., Visium, Xenium, Geo Mx/G4X), computational and virtual staining, natural language processing of pathology reports, large‑scale imaging analysis, and integrative statistical modeling for disease prognostication and treatment response prediction across multiple cancer types.

While research mentorship spans both institutions,
new doctoral and postdoctoral positions are offered exclusively through Cedars‑Sinai Medical Center
.

Doctoral training opportunities are available through Cedars‑Sinai Medical Center and focus on methodological and applied research at the intersection of spatial biology, computational pathology, and clinical data science. PhD trainees work on projects involving spatial transcriptomics‑based modeling of cancer progression and therapy response, digital and virtual staining technologies, natural…