Internships at Los Alamos National Lab

Mónica Ivelisse Feliú-Mójer's picture

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  • Parallel Computing Summer Research Internship 
  • Providing students with a solid foundation in modern high performance computing (HPC) topics integrated with research on real problems encountered in large-scale scientific codes
    Target Student:  Upper-level undergraduate and early graduate students; http://parallelcomputing.lanl.gov

     
  • Computer System, Cluster, and Networking Summer Institute (CSCNSI) 
  • Learn the basics of high performance computing system administration. Students work in small project teams to execute real-world projects on computer clusters that they have assembled and configured.
    Target Student:  Upper-level undergraduate and early graduate students; http://clustercomputing.lanl.gov
     
  • Co-design School 
  • Team research project for graduate students from varying backgrounds (usually CS, computational physics, and mathematics) to work on a computational co-design topic, such as novel programming models on a specific application, such as Hydro- and Molecular dynamics.
    Target Student:  Upper-level graduate students; http://codesign.lanl.gov
     
  • Data Science at Scale School 
  • The Data Science at Scale School is active year round to recruit outstanding students to the laboratory to participate in data intensive science projects. Particular focus is placed on using big data technologies to gain insights from science data.
    Target Student:  Upper-level undergraduate and graduate students; http://datascience.lanl.gov

  • Cyber Security Summer School
    Students will learn the necessary concepts and skills for cyber incident response.  In addition to classroom training and lectures, students will spend most of their time working with a mentor on a small team project.
  • Target Student:  Junior, Senior, or Master's student; http://cyberfire.lanl.gov/toaster.html
  • Applied Machine Learning Summer Research Internship
    Team research projects for graduate students from varying backgrounds (computer science, statistics, mathematics, or domain science fields) to apply machine learning methods to real-world scientific data analysis problems.
    Target Student: 
    Upper-level Graduate students;http://aml.lanl.gov
  • Quantum Computing Summer School Internship
    Participants will be exposed to the theoretical foundations of quantum computation and will become skilled at programming commercial quantum computers, such as those developed by D-Wave Systems and IBM.
    Target Student:  Upper-level undergraduate and early graduate students; http://quantumcomputing.lanl.gov

LANL student summer fellowships:

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