Job Details

Big Data Mining And Artificial Intelligence In The Era Of Large Astron

Greenbelt, Maryland, United States

The 2010 Decadal Survey; New Worlds, New Horizons in Astronomy and Astrophysics (National Research Council, 2010), prescribed the Wide-Field Infrared Survey Telescope (WFIRST) as its highest ranked large space project for the decade. WFIRST, conceived as a filled aperture, wide field, near infrared flagship-class observatory, was proposed to settle fundamental questions about the nature of dark energy and to complete the census of all possible types of exoplanets in the Milky Way galaxy using wide-field imaging onto a very large, dense detector array. To accomplish this, the mission will accumulate 256 trillion pixel images at a rate of one every 15 minutes for analysis.
The Transiting Exoplanet Survey Satellite (TESS), launched in early Spring of 2019, is conducting wide-field imaging of nearly the entire celestial sphere in search of exoplanets that eclipse their host star. Tiling the sky over a period of 2 years using four large detector arrays, TESS will recover about 2.5 million photometric lightcurves for each of its 26 sectors before moving on to a survey of the Galactic plane. The community, using a crude box-least squares method, is searching this vast trove for the faint signature of exoplanets - simply discarding other sources of stellar variability as noise.
These two missions and others under consideration have one thing in common - very large, very complex data sets replete with observatory systematics and elusive, tenuous astrophysical signals. Recognizing the significant challenge this raises for the science return of TESS, WFIRST and future survey missions, NASA seeks a post doctoral fellow with the necessary data science skills to formulate a systematic approach to extraction of science in the face of extremely high data rates or in large complex archives.
Examples of areas of interest include:
Machine learning for classification of astrophysical signals
Artificial intelligence augmentation of spaceborne observatories to reduce data transmission rates
Migration of science processing to cloud-based computing
Development of highly accessible data bases with in situ computing
Graphics Processing Unit acceleration of scientific data processing
Candidates need not have a strong background in astronomy or physics to apply. Candidates must have experience or training in one or more of the following skill sets in the service of, or scalable to very large data archives or high data accumulation rates:
Data science
Computer science
Machine learning
Artificial intelligence
Database design

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