DANCE/CoDaS@Snowmass 2022 computational and data science software training


The joint DANCE/CoDaS@Snowmass 2022 computational and data science software training event will take place from Tuesday 19 July through Saturday 23 July, 2022 at the University of Washington. The training event will be co-located with the Snowmass Community Summer Study in Seattle, WA.  

The DANCE/CoDaS@Snowmass event is a collaboration between the Dark Matter and Neutrino Computation Explored (DANCE) consortium and the Computational and Data Science for High Energy Physics school.

Advanced software is a critical ingredient to scientific research. Training young researchers in the latest tools and techniques is an essential part of developing the skills required for a successful career both in research and in industry.

DANCE/CoDaS@Snowmass 2022 is aimed at senior graduate students and/or early postdocs (in their first year) who have a solid base of software skills and are interested in developing more advanced skills as well as participating in the Snowmass Community Summer Study. Applicants should be affiliated with a U.S. university (students/postdocs) or laboratory (postdocs).

Specific topics to be covered at the school include:

  • The Scientific Python ecosystem for particle physics
  • Advanced Data Analysis tools in the Python Ecosystem
  • Machine Learning, Bayesian Networks
  • Statistical tools for particle physics

The school offers a limited number of young researchers an opportunity to learn these skills from experienced scientists and instructors. Successful applicants will receive travel and lodging support to attend the school.

The training activities will take place in the mornings of 19-23 July and participants will be free to engage with the larger Snowmass community activities in the afternoons. Participants are expected to arrive on or before 18 July and remain through the end of the Snowmass event on 26 July.

This project is supported by National Science Foundation grants OAC-1829707, OAC-1829729, OAC-1836650, OAC-2017699 and OAC-2017760,  and the Enrico Fermi Institute at the University of Chicago. Any opinions, findings, conclusions or recommendations expressed in this material are those of the developers and do not necessarily reflect the views of the National Science Foundation.