Project Description: We use innovative techniques, including Raman spectroscopy and image processing, to analyze rock and mineral samples. Resulting data is used to develop automated mineral, sediment and rock classifiers, and to identify biosignatures as part of an automated science analysis system to be used on future rover missions. Students would work to acquire images and Raman spectra of our samples and perform hand sample and/or thin section analysis. Students with a good machine learning background could also work towards improving our automated classifier algorithms and towards building an integrated automated science analysis system. Additional tasks would be to create an online spectral and imaging database with analysis tools of our rock and mineral library. Background in geology, earth or planetary science, computer science, electrical engineering, and/or physics preferred. Geology students with a background in mineralogy and petrology with thin section analysis experience desired.
NASA Relevance: Developing a mineral and multi mineral database of Raman spectra and automatic science analysis classifiers for Mars and lunar surface studies.
Work Description: Geology student with mineralogy and petrology background might analyze samples in hand sample, thin sections of samples with a thin section microscope, and help image samples upclose for developing image analysis algorithms.
Computer science/Engineering student with MI, AI, computer vision, and multi-signal processing experience might help to develop a reasoning system, or help improve on the image analysis or spectral algorithms. Mentor would help students work with the Raman spectrometer to obtain Raman spectra of samples.
Open or Reserved Project: Open, 2 positions