Kavli Institute for Particle Astrophysics and Cosmology (KIPAC) at Stanford University and SLAC National Accelerator Laboratory with a focus on the Large Synoptic Survey Telescope (LSST) and LSST Dark Energy Science

Submission Information
Publish Date: 
Wednesday, September 11, 2019
Archive Date: 
Wednesday, November 20, 2019
Job Summary
Job Category: 
Post-doctoral Positions and Fellowships
Institution Classification/Type: 
Large Academic
Street Line 1: 
452 Lomita Mall
Street Line 2: 
M/C 4085
United States of America
Job Announcement Text: 

Applications are invited for a postdoctoral position at the Kavli Institute for Particle Astrophysics and Cosmology (KIPAC) at Stanford University and the SLAC National Accelerator Laboratory with a focus on the Large Synoptic Survey Telescope (LSST) and the LSST Dark Energy Science Collaboration. We seek applicants with a background in observational cosmology, particle astrophysics, or hardware development, who are highly motivated to engage in preparing for LSST data using a combination of simulation tools, precursor data sets, and early camera or commissioning data to identify and understand potential systematic effects that, if uncorrected, could limit our ability to extract accurate information from images and subsequently constrain cosmological parameters and the physics of the Universe. Strong computational abilities and/or experience with instrument or pipeline design and development are highly desired. Depending on the interests of the candidate, the work could focus on atmospheric, optical, or detector effects, or challenges such as overlapping objects in images (blending), photometric calibration, point spread function estimation, and photometric redshifts. A goal of the research is to quantitatively assess potential biases and develop solutions to meet the stringent requirements for dark energy probes with LSST, on a timescale that could enable incorporation into the LSST data management pipeline or operations planning. Ability to work well in a collaborative project and clearly document results is critical.

The successful candidate will have the opportunity to work within the LSST Dark Energy Science Collaboration (DESC) and will benefit from interactions with scientists at the SLAC National Accelerator Laboratory who work on the LSST camera and data management projects. Opportunities exist to work with the LSST camera team on camera test data, and to prepare for and participate in LSST system commissioning. KIPAC provides a stimulating intellectual environment for interactions with scientists pursuing observational, experimental, computational, and theoretical research relevant to the exploration of dark energy with LSST, as well as with DES, DESI, and CMB experiments.

Candidates must have a Ph.D. in physics or astrophysics. The position is available beginning Summer/Fall 2020 or earlier. Applications are due November 15, 2019.

Interested applicants should submit a curriculum vitae, publication list, and a description of research interests and skills using Academic Jobs Online: https://academicjobsonline.org/ajo/jobs/14522. In addition, applicants should arrange for three letters of recommendation to be submitted by the reference writers through the Academic Jobs Online website.

For further information regarding the positions, contact one or more of the faculty or staff scientists working on LSST at Stanford and SLAC, including Professor Steve Allen, Professor Patricia Burchat, Dr. Daniel Gruen, Professor Steve Kahn, Dr. Phil Marshall, Dr. Kevin Reil, Professor Aaron Roodman, or Professor Risa Wechsler.

Stanford University and SLAC National Accelerator Laboratory are affirmative action and equal opportunity employers, committed to increasing the diversity of their workforces. We encourage applications from candidates from groups that are underrepresented in astrophysics and cosmology or who will bring additional dimensions to Stanford and SLAC’s research and educational missions.

Application Deadline: 
Friday, November 15, 2019
Current Status of Position: 
Accepting Applicants
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