Dear Colleagues,
We are advertising two postdocs at BNL, see descriptions below. Sorry in
advance for the spam.
Best Regards,
anže
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 ([log in to unmask]). 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
[log in to unmask] 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:
https://jobs.bnl.gov/job/upton/post-doctoral-research-associate-physics/3437/5344803
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:
https://jobs.bnl.gov/job/upton/postdoctoral-research-associate-physics/3437/5344804
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.
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