The Department of Astrophysical Sciences, Princeton University, invites applications for one or more postdoctoral, senior research, or specialist positions in statistical astronomy. The successful candidate(s) is expected to carry out original research in statistical analysis or machine learning of large survey data in collaboration with Princeton faculty, postdoctoral researchers and students.
The positions are funded by the Schmidt Futures Foundation as part of the AI Accelerator program (https://schmidtfutures.com/our-work/scientific-knowledge/ai-accelerator/). It seeks to increase the utilization and yield of research data by incorporating modern machine learning methods. One of our main interests is the optimization of the target selection for the upcoming Prime Focus Spectrograph (PFS) Strategic Survey on the Subaru 8.2 meter telescope on Maunakea, Hawaii, based on the information content of the observations. More generally, we seek innovative ideas for data collection or utilization, and particularly encourage applicants with a strong background in statistical modeling and machine learning to apply.
The successful candidate(s) will join the Astronomical Data Group, led by Peter Melchior, Robert Lupton, Jenny Greene, Michael Strauss, and Jim Gunn, and benefit from the active role our department plays in many scientific and technical aspects of several large surveys, as well as the collaboration with the Center for Statistics And Machine Learning (CSML)(https://csml.princeton.edu/).
The department is playing a major role in the Large Synoptic Survey Telescope (LSST) consortium, which is building a dedicated 8.4-meter telescope to carry out a 20,000 square degree multi-band and multi-epoch imaging survey. It is collaborating with the National Astronomical Observatory of Japan to carry out deep, high-resolution, wide-area imaging and spectroscopic surveys to study galaxy evolution, cosmology, Milky Way structure, and planetary systems on the Subaru telescope as part of the Hyper Suprime-Cam, Prime Focus Spectrograph and Charis surveys. We have major initiatives searching for and characterizing extrasolar planets with the Transiting Exoplanet Survey Satellite (TESS) and the HAT surveys. We are using the Atacama Cosmology Telescope (ACT) to map the intensity and polarization of the Cosmic Microwave Background at a variety of frequencies, and are participating in the new Simons Observatory for CMB studies. Finally, we expect to be involved in the analysis of data from the Wide Field Infrared Survey Telescope (WFIRST).
The successful candidate(s) will have access to those data sets, state-of-the-art computational facilities, and a broad range of collaborators in the department and through CSML. They will also be encouraged to collaborate with our colleagues at Johns Hopkins University and the Institut d’Astrophysique de Paris, and other teams of the AI Accelerator program.
Interested persons should submit a curriculum vitae, bibliography, a concise statement of research interests (maximum 3 pages), and provide contact information for three references by November 4, 2019. Applicants must apply via the web at: <LINK TBD>. Letters of recommendation will also be handled through this site. All applications received by November 4, 2019 will be fully considered, but applications will continue to be accepted until the position is filled. All applications will be considered for all postdoctoral positions available in the department, but you will be asked in the application which positions you are interested in.
Appointments will be made to the research staff at a level and salary commensurate with experience, for an initial period of one year, renewable annually based on satisfactory performance, for a total of up to three years. This position is advertised subject to funding. An advanced degree in Astronomy, Statistics or a related field is required. For further inquiries, contact email@example.com.
This position is subject to the University's background check policy. Princeton University is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, or any other characteristic protected by law.