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We typically post internship opportunities through the NASA STEM Gateway or the NASA Pathways Internship.
News: A NASA Pathways Internship opportunity was launched to support a undergraduate or graduate student to develop advanced machine learning models on the NASA computing cloud. The application deadline is September 16, 2022. Applicants can apply here: https://www.usajobs.gov/job/675779600 More information on Pathways internships is available here: https://www.nasa.gov/careers/pathways
Enhance the visualization style of, and information communicated through, a desktop GUI tool that summarizes the key differences between coronal and solar wind model predictions and ground truth satellite imagery and in situ measurements. How can these images and solar wind measurements be visually compared to model predictions in a way that highlights the important differences, while giving users control over the viewing experience?
The project's primary goal is to identify and classify flux rope signatures from ICME catalogs and insitu measurments. The student would analyze ICMEs and flux rope characteristics learning them from physical flux rope models and doing a study with analysis of flux rope types during the solar cycle, direction, orientation, etc. The project would involve the intensive use of python and IDL for data loading, visualization, and analysis.
Develop a Bayesian method for uncertainty quantification of forecasts made by a solar wind model (WSA). Given the prediction and its context, what is the ideal confidence interval to attach to the point-prediction? Themes are statistics, ensemble modeling, and experimental design/execution.
Create a registry of python access “quick start” scripts for Heliophysics System Observatories. Data from different sources have different forms and different query parameters. The quick start script will query the data source, access and display the data, and note common artifacts (such as calibration exercises, missing data, anomalies). Optional analysis component: perform analysis on heliophysics data sets to examine the properties of different types of phenomena.
Build Knowledge Graphs from Heliophysics papers to gain greater understanding. Using the Helio 2050 (program: https://www.hou.usra.edu/meetings/helio2050/program/) whitepapers will build a KG to understand content of submissions in terms of their relatedness to one another, and how authors are related to various topics among other things. This will aid in interpreting these whitepapers and how we may contribute to the 2024 Decadal Survey.
Understanding the radiation environment in LEO orbit has multiple identified users. Currently in situ data is often used and we will work towards the requirement of forecasting this index for intervals of 1hr, 1 day, and 3 days in advance.