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Informatics and Data Science Intern

Department: 2210 - Informatics and Data Science
Location: Seattle

Title: Machine learning models for cell type analysis

Program Overview

Students will be matched with mentors and projects based on degree of experience and mutual interests. In addition to working on a specific project, Allen Institute interns will meet regularly as a group in sessions designed to augment their research experience. These meetings could include sessions aimed at improving communication skills, opportunities to share their projects with peers, and discussions with Allen Institute scientists and staff representing different disciplines within the organization. At the end of the program, each intern will write up a project summary and give a presentation summarizing their work.

Project Summary:

Learning low dimensional representations to uncover structure in high dimensional transcriptomic, electrophysiological, and morphological characterization data is a central goal of the cell types project. Machine learning models developed by our group have thus far been used to obtain promising representations for transcriptomic data to this end. It has been important to tune models for different data modalities and experimental techniques, to not only ensure that models are robust and reproducible, but also to realize the full potential of rich datasets produced by experimental teams at the Institute. Computational resource requirements for such research vary drastically over the course of development, from requiring a few GPU hours for simple checks to requiring hundreds or even thousands of cumulative GPU hours to understand how model hyperparameters can influence results. Such computational experiments can, in principle, be performed efficiently and in parallel if multiple GPUs and custom computational environments can be summoned on demand. Leveraging computational resources in the cloud is a cheap and efficient alternative to acquiring and managing additional resources at the Institute. Moreover, such strategies could be used to extend the utility of the models we develop for members of the academic community, who may want to train our models with custom datasets. The intern would contribute towards implementing a scalable strategy that would enable model development with minimal overhead. The intern would use such a proof-of-principle implementation to explore models that process raw time-series and imaging datasets to uncover features that may be used for electrophysiology- and morphology-based cell type classification.

Educational Objectives:

  • Deploy machine learning models at scale
  • Analyze electrophysiological time-series and/or light microscopy images with machine learning models
  • Contribute to representation learning research relevant for the cell types project


  • U.S. Citizen or U.S. Permanent Resident currently enrolled in an accredited U.S. college or university.
  • Enrolled in undergraduate studies or in a Masterís program. You must be enrolled and returning to school Fall quarter.
  • Must be able to start on either June 3 or June 17th, 2018 and commit to the full 10-12 week internship time period. Interns will be expected to work 35-40 hours a week.
  • Must be 18 years of age or older.


Interns will receive $15.00 per hour which is equivalent to $6,000 less taxes.


Housing is not provided as a part of the internship program. If selected we will provide a list of housing resources for you.


Interns will be provided with an ORCA business passport free of charge for the duration of their internship.

How to Apply

  1. Complete an application through this portal. The application deadline is January 30, 2019.
  2. Submit a resume as well as the following documents using the + supplemental documents button:
    • A personal statement describing your interest in Allen Institute for Brain Science
    • College transcript with Cumulative GPA and Courses taken (can be unofficial)

3. Send one letter of recommendation to

Contact us

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