The Los Alamos National Lab’s Data Science at Scale School was inaugurated in 2013 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 scientific data. Although most students are present at the lab for 10-12 weeks in the June to August timeframe, the schedule is flexible to meet individual needs and the school is active year round.
APPLICATIONS NOW OPEN FOR 2021
Outstanding upper-level undergraduate or graduate students in computer science, data science, math or statistics are encouraged to apply for (paid) internships in the Data Science at Scale Summer School. See below for Research Focus Areas. Students need not be US citizens. Priority will be given to students applying by Feb 15, 2021. Applications will be accepted through March 15, 2021.
Research Focus Areas
The 2021 Data Science at Scale Summer School is currently recruiting candidates for the following Research Focus Areas:
In situ Data Analysis and Visualization Workflows
As high performance computing moves into exascale range, the post hoc analysis paradigm will shift to in situ. In situ data analysis and visualization selects, analyzes, reduces, and generates extracts from scientific simulation results while the simulation is running to overcome bandwidth and storage bottlenecks. The results are scientific workflows that combine in situ and post hoc analysis into a full pipeline.
Projects in this focus area will give students the opportunity to build sophisticated pipelines to run simulation codes on HPC resources; develop in situ analysis and visualization algorithms; and apply compression techniques to real world data. Complementary projects will focus on post hoc reconstruction, analysis techniques, and validation of the in situ workflows.
Machine Learning for Data Science and Visualization
Machine learning techniques have become an important analytical tool for data science in recent years. Projects in this focus area will give students hands-on experience in applying ML/AI techniques to various novel data analysis and visualization problems such as in situ feature exploration in scientific simulations, image analysis, uncertainty quantification, data reduction, and integrating ML/AI techniques into analysis workflows.
Other areas of research interest
Data Science at Scale mentors have expertise in areas such as vector topology, scientific visualization applications, human perception in visualization, color theory, interactive visualization techniques, uncertainty quantification. Funding for research projects in these areas may become available and we welcome applications focused on these areas.