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SURF: Announcements of Opportunity

Below are Announcements of Opportunity posted by Caltech faculty and JPL technical staff for the SURF program. Additional AOs for the Amgen Scholars program can be found here.

Specific GROWTH projects being offerred for summer 2018 can be found here.

Each AO indicates whether or not it is open to non-Caltech students. If an AO is NOT open to non-Caltech students, please DO NOT contact the mentor.

Announcements of Opportunity are posted as they are received. Please check back regularly for new AO submissions! Remember: This is just one way that you can go about identifying a suitable project and/or mentor.

Announcements for external summer programs are listed here.

Students pursuing opportunities at JPL must be
U.S. citizens or U.S. permanent residents.

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Project:  Onboard Data Analysis to Maximize Europa Clipper Science Return
Disciplines:  Computer Science, Planetary Science
Mentor:  Gary Doran, (JPL),, Phone: (818) 354-5749
Background:  The Europa Clipper mission will carry a suite of scientific instruments capable of collecting vast amounts of information during a series of approximately 40 flybys of the Jovian moon Europa. Due to the high radiation environment around Jupiter, the Clipper will spend most of its time far away from the planet, collecting most of its data during fast flybys once every few weeks. This observation strategy precludes making specific targeted measurements of possible transient events such as plumes that might change more quickly than the multi-week observation cadence. Furthermore, the large Earth-Jupiter distance severely constrains the amount of data that can be downlinked from the spacecraft. Thus, we are investigating approaches to perform data analysis onboard the spacecraft to (1) identify the most scientifically relevant or interesting data to downlink first, (2) analyze data quality to avoid downlinking data that has been corrupted in the high-radiation environment, and (3) identify thermal and spectral anomalies such as hot-spots, plumes, or deposits of organic materials at the surface. The identification of anomalies can enable either downlink prioritization or re-targeting of other instruments during flyby to collect more detailed information about a phenomenon.
Description:  In this project, you will work with real and simulated data to develop algorithms for onboard data analysis to help achieve one or more of the three goals described above. Datasets will be chosen to serve as analogues for data to be collected by the E-THEMIS (a thermal imager), MISE (an imaging spectrometer), PIMS (used for acquiring magnetic field measurements), or UVS (an ultraviolet spectrometer) instruments planned for the Europa Clipper. Existing analog datasets include AVIRIS-NG (Earth), THEMIS (Mars), CIRS and VIMS (Saturn), and NIMS (Jupiter). You will work with scientists and engineers to ensure that your developed solution captures scientifically relevant information and can be implemented under the limitations imposed by the Europa Clipper flight or instrument computers. There will be an opportunity to write up your work in a paper for publication in a relevant machine learning or planetary science conference or journal.
References:  Europa Clipper Mission Page:
Related work on thermal anomaly detection for THEMIS at Mars:
Possible future observations given hypotheses of physical processes occurring at Europa:
Student Requirements:  Required: Proficiency in Python for developing a prototype algorithm implementation; experience working in a linux/unix development environment; relevant coursework in statistical machine learning/artificial intelligence and computer science; ability to communicate effectively and work with a team
Suggested: Introductory level knowledge in physics or planetary science
Location / Safety:  Project building and/or room locations: . Student will need special safety training: No.
Programs:  This AO can be done under the following programs:

  Program    Available To
       SURF    both Caltech and non-Caltech students 

Click on a program name for program info and application requirements.

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