The Jackson Laboratory

Machine Learning Student Intern


PayCompetitive
LocationRemote
Employment typeFull-Time

This job is now closed

  • Job Description

      Req#: JR004449

      Summary

      The ML and Imaging team within the Computational Sciences (CS) department at The Jackson Laboratory (JAX) is looking for a motivated summer intern interested in spending 10-12 weeks working on graph neural network (GNN) interpretability. Our group has ongoing efforts to develop deep learning methods for integrating multi-omics and cross-species data to enable translational research. The intern will have the opportunity to work under the supervision of computational scientists on the existing effort toward developing and implementing methods for improving GNN explainability.

      About Your JAX Internship Experience

      Join peers selected from educational institutions across the country for a summer of professional growth and career exploration. As a JAX Intern, you will receive a dedicated mentor in your department and connect with your fellow interns through JAX-sponsored activities.

      The ideal start date for this position is by June 3, 2024.

      Key Responsibilities

      • Survey the relevant research literature space (deep learning, biomedicine, AD, multi-omics/cross-species data integration, model interpretability, etc.)
      • Implement and experiment with GNNs and GNN explanation methods applied to omics data.
      • Present findings periodically in group meetings
      • Participate in other lab activities (journal clubs/seminars, etc.)

      Desired Qualifications

      JAX encourages applications from a diverse mix of educational backgrounds and experiences. Preferred qualifications include:

      • Must be enrolled in a college or university program
      • Junior/Senior undergraduate, or graduate student pursuing a degree in Computer Science, Bioinformatics, or a related field
      • Experience with programming in Python
      • Familiarity with machine learning and deep learning
      • Practical experience with ML modeling
      • Some research experience at the intersection of ML and biomedicine is a plus

      Submission of a cover letter is required for this application.

      The hourly rate is $17.50 - $25.00. Hourly rate will be determined based on qualifications and experience.

      #LI-MJ1

      #CA-MJ4

      About JAX:

      The Jackson Laboratory is an independent, nonprofit biomedical research institution with a National Cancer Institute-designated Cancer Center and nearly 3,000 employees in locations across the United States (Maine, Connecticut, California), Japan and China. Its mission is to discover precise genomic solutions for disease and empower the global biomedical community in the shared quest to improve human health.

      Founded in 1929, JAX applies over nine decades of expertise in genetics to increase understanding of human disease, advancing treatments and cures for cancer, neurological and immune disorders, diabetes, aging and heart disease. It models and interprets genomic complexity, integrates basic research with clinical application, educates current and future scientists, and provides critical data, tools and services to the global biomedical community. For more information, please visit www.jax.org .

      EEO Statement:

      The Jackson Laboratory provides equal employment opportunities to all employees and applicants for employment in all job classifications without regard to race, color, religion, age, mental disability, physical disability, medical condition, gender, sexual orientation, genetic information, ancestry, marital status, national origin, veteran status, and other classifications protected by applicable state and local non-discrimination laws.

  • About the company

      The Jackson Laboratory is an independent, nonprofit biomedical research institution. It employs more than 2,100 employees in Bar Harbor, Maine; Sacramento, California; and at a genomic medicine institute in Farmington, Connecticut. Leads the search to cure diseases rooted in our DNA.

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