Jobs in BNL Cosmology Group

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We are conducting a wide search for two post-docs at Brookhaven National Lab. BNL is a multipurpose research institution within the DOE lab complex in Upton NY. It is commutable from NYC and the Hamptons. We are involved in a number of currently funded DOE experiments (eBOSS, DES, DESI, LSST) and planning for the future through a small 21-cm intensity mapping effort. For LSST, BNL is responsible for the construction and testing of the Science Rafts. We collaborate closely with Stony Brook University and NYC based institutions (NYU, Columbia, Flatiron institute). Both positions are for standard 2+1 yrs and come with competitive salary and benefits. The start dates are flexible and early start is preferred, but we expect successful applicants to join us no later than fall 2018.

For full consideration, applications should be submitted before December 11, 2017. Any inquires regarding the position should be sent to Anže Slosar ( In order to speed up the process, interested candidates should send the application pack (cover letter, CV, 3 page proposal) and arrange for 3 reference letters to be sent to Generic applications are fine. Successful candidates will be capable of working independently,  generate their own research ideas and will have good coding skills.

Job #1, ID 1094:

The focus of this job is to prepare for the LSST data by working on the methodology and software necessary for a successful multi-probe analysis, combining large scale structure and weak gravitational lensing data. The pipeline will be tested on precursor data, such as public DES, HSC, and related datasets.

Job #2, ID 1095:

The focus of this job is to find applications for machine learning and related statistical methods on DOE High Energy Physics (HEP) Cosmic Frontier problems. This job is funded by a SciDAC project and a successful candidate will work with both HEP and computer scientists from BNL’s Computing Science Initiative. The focus is broad: from applications to optical image analysis and deblending, optical sky modelling, to CMB Stage 4 foreground separation and large scale inference problems. Successful candidate will be an astronomer or a cosmologist with strong interest in inference and statistics.