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Job Summary
This position entails modeling of the atmosphere of Uranus, using Hubble Space Telescope (HST) images as input into radiative transfer models. The ultimate objective is to calculate an improved estimate of the Bond albedo of Uranus, with well characterized statistical assessments of the uncertainties of the estimate(s).
Essential Job Duties
- Gather key data from the HST archive and calibrate/process the images such that the quantitative analysis of the atmosphere may proceed.
- Utilize existing optical/ultraviolet radiative transfer codes to produce artificial Uranian atmospheres whose properties can be compared to HST image data.
- From these first two duties, constrain the properties of the atmosphere such that the Bond albedo may be estimated, with quantitative estimates of the uncertainties.
- Write up the results of these studies for the larger HST-funded science team and prepare texts and figures for scientific talks and peer-reviewed publications.
- Other duties as assigned.
Basic Qualifications
- PhD in Planetary Science or Astronomy, with familiarity with Hubble Space Telescope image data.
- Working knowledge of the radiative transfer modeling of planetary atmospheres, both in broad wavelength coverage imaging and spectroscopy of the atmosphere(s).
- Expertise with methods of Bayesian inference as relevant to the assigned atmospheric modeling.
- Expertise with computational implementations of atmospheric radiative transfer, on Linux-based workstations and large High Performance Computing Clusters.
Preferred Qualifications
- Experience in Machine Learning analysis of complex image data sets.
- Experience with spaceborne and ground-based astronomical imaging data sets and approaches.
Required Documents