UC Merced joins LLNL for a Data Science Challenge

June 1–12, 2020

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Data Science Institute

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Suzanne Sindi, UC Merced | 209.228.4224

Marisol Gamboa, LLNL | 925.423.2472

Jennifer Bellig, LLNL admin | 925.424.5197

You could be part of the “the smartest square mile on earth” this summer!

We’re building multidisciplinary teams to tackle real-world data science challenges. And we need 20 of UC Merced’s finest scholars to make it happen.

Apply by March 1, 2020: llnl-data-science-challenge [at] llnl.gov

Your application must include:

  • Statement of interest (1 page)
  • Resume
  • 1 letter of recommendation by a UC Merced faculty member sent on your behalf to the e-mail address above (subject line: "Recommendation Letter <Your Last Name>")
  • Graduate students: Include example(s) of your leadership and research experience in your statement of interest. The recommending faculty member should be your advisor.

What it takes

  • Undergraduates interested in data science or related disciplines
  • Graduate students experienced in research or applying skills to a research environment
  • Students actively pursuing a degree in mathematics, computer science, engineering, science, or other relevant fields
  • Students with computational experience

What to expect

Don’t miss this unique opportunity! The experience will be unlike any other. This intensive training program provides hands-on exercises and assignments, tours, and seminars. You’ll work on an important data science problem while learning from experts, networking with peers, and developing skills for future internships. You’ll also get a taste of day-to-day life at LLNL, where we have a passion for national service. Read about the 2019 UCM Challenge.

You’ll solve an exciting problem in materials science. Energetic materials detection is a core competency needed to protect civilians and U.S. infrastructure from malicious adversaries. As such, there is a compelling need to reliably detect and differentiate energetic materials (both known and unknown) from a complex background of diverse chemical compounds. In this challenge, you will apply machine learning techniques to train models that can identify energetic chemical compounds using atomic and/or molecular features from available data sources such as CSD, ZINC, and QM9. Read about your LLNL mentors.

Accommodations will be provided, including lodging, food stipend, and travel reimbursement (to/from UCM).

Undergraduate students will:

  • Work with scientists, engineers, and technical staff to support to projects in computational science, numerical methods, mathematics, and materials science
  • Participate in a real-world materials science data problem
  • Gather and analyze data in support of scientific research
  • Participate in research and challenge problem evaluation discussions
  • Present results to scientists, engineers, and technical staff during final student briefing
  • Attend every day of the 2-week challenge

Graduate students will:

  • Serve as team lead, guiding the research direction for 4 undergraduate students
  • Provide advanced technical support to scientists, engineers, and technical staff in materials research and development
  • Demonstrate work via presentations and/or poster sessions
  • Attend every day of the 2-week challenge