Data Science at Scale

Pioneering innovative data solutions using cutting edge techniques for the nation’s top scientific problems

Applications open for Summer 2025 internships. Deadline: February 5

The Data Science at Scale Summer School was inaugurated in 2013 to recruit outstanding students to the laboratory to participate in data intensive science projects. Students work on topics such as machine learning, data compression, large data analysis, scientific visualization, topological data analysis, etc. for large scientific data analysis. The school starts approximately mid-May and runs through mid-September. Students pick a 10-12 week window in this time frame to conduct their summer internship on a specific project as part of the team. This flexibility allows for differences in quarter vs. semester school schedules and specific needs of the internship project.

Students

Outstanding upper-level undergraduate or graduate students in computer science, data science, math, or statistics or applied computational science are encouraged to apply for (paid) internships in the Data Science at Scale Summer School.

Students work together with computer and application scientists to produce prototypes that solve data intensive scientific problems of interest to the laboratory. Summer interns will learn hands-on by engaging in scientific research using visualization and data analysis technologies. Their code and reports are (usually) published as open source and composed into a suite of programs that help characterize the growing data intensive science workload at Los Alamos.  Students will have opportunities to work on high performance computing clusters, apply visualization tools, and gain experience in communicating their work through discussions and weekly presentations.  Students will attend seminars by LANL researchers and external visitors.   

This multidisciplinary program is designed for graduate and upper-level undergraduate students from all science, math, computer science, and technology fields who are interested in gaining insight into scientific data.  Experience with programming (C++, VTK, VTK-m, Python) is encouraged. 

Students will work one-on-one with mentors & co-mentors or in small collaboration teams. 

Every year, the data science topics the DSS Team covers changes. This year, we expect internships to be available on the following topics:

1. Ensemble data management and analysis (including building tools for/with large data ensembles)
2. Development of machine learning models for scientific data
3. Development of front-end visualization tools for ensemble data
4. Foundation models for scientific data

Application Process

To apply, you will need to submit the following materials:

• Cover letter detailing your background and how an internship in the DSS Summer School will forward your career goals
• Current resume / CV
• Unofficial university transcripts (official transcripts will be required if a position is offered and accepted)

DSS Summer School applications are through the general Computational Sciences job ads at  www.lanl.jobs.  Please apply to the appropriate posting.  The posted job ad IRC numbers are updated several times a year so please look for jobs titles similar to:

• Computer, Computational, and Statistic Sciences Graduate Intern
• Computer, Computational, and Statistic Sciences Undergraduate Intern

Be sure to indicate your interest in the Data Science at Scale Summer School in your cover letter.

Review process

The review process begins once a complete application is submitted.  Applications are shared with potential mentors.  Mentors are responsible for contacting applicants for interviews and subsequent offers.  Due to the volume of applicants, not all applicants will be contacted.  The review process is generally concluded by March 30. 

Duration & Location

The summer program takes place in the June - August time frame. Students are encouraged to conduct their internships on site at Los Alamos, NM. Remote positions may be available for special circumstances. 

Eligibility Requirements

• Must be accepted to or enrolled in a graduate or undergraduate degree program.
• Must meet LANL requirements of a cumulative G.P.A. of 3.2
• Must be available to live and work in the United States.