PPA HEP Group, Stanford University

Position ID:Stanford-SLACPPA-RAMACHLEARN [#11969]
Position Title: Research Associate - Machine Learning for LHC, Neutrino, and Cosmology Experiments
Position Type:Postdoctoral
Position Location:Menlo Park, California 94025, United States [map]
Subject Area: High Energy Physics / Experiment
Appl Deadline:2018/10/31 (posted 2018/09/13, listed until 2019/03/13)
Position Description:    

The SLAC National Accelerator Laboratory (SLAC) is seeking one Research Associate to work on Machine Learning applications for experiments at each of the Energy, Intensity, and Cosmic frontiers, as part of a new cross-frontier research team. This includes Machine Learning applications for the ATLAS experiment at the Large Hadron Collider (LHC) at CERN, accelerator neutrino oscillation measurements with liquid argon time projection chamber (LArTPC) detectors such as MicroBooNE, ICARUS and DUNE, and for the Large Synoptic Survey Telescope (LSST). A significant focus of this work will be on event and image reconstruction and analysis, potentially making use of Machine Learning techniques in Computer Vision, Sequence Analysis, and Generative Modeling. The experimental program at SLAC present ideal opportunities for developing applications of machine learning in high energy physics and cosmology, fields in which the development of these techniques is rapidly expanding. This opening allows participation in a selection of experimental areas that SLAC is involved in, and a variety of physics topics.

Current areas of ML in HEP research at SLAC include physics data reconstruction, object classification, and generative modeling for fast simulation, utilizing deep neural networks (convolutions, recurrent, graph-based, etc.) for LArTPC, LSST, and LHC experiment

The SLAC ML group in HEP and Cosmology consists of 4 PI’s, Michael Kagan, Phil Marshall, Ariel Schwartzman, and Kazuhiro Terao, as well as students from each of the frontiers. The successful candidate will work with students in all frontiers to purse ML driven projects. More information on the SLAC ATLAS group can be found here http://www.slac.stanford.edu/exp/atlas/, on the SLAC Neutrino group here http://www.deeplearnphysics.org, and the SLAC LSST group here https://lsst.slac.stanford.edu/

Position duties:
• Work on ML applications across Energy, Intensity, and Cosmology frontiers.
• Guide students indevelopming ML applications for their respective experimental focus
• Conduct original research independently and in collaboration with the SLAC ML in HEP team
• Prepare research papers for publication, and present research findings at conferences and workshops.

Qualifications:
These are highly competitive positions as part of the general research associate program at SLAC, requiring a background of demonstrated excellence in research and a PhD in physics, cosmology, computer science, or related field, or have completed the requirements for such a degree prior to starting the position. Experience with research in Machine Learning and the associated research tools (such as Tensorflow, Keras, PyTorch, etc.) is highly beneficial. The position is initially a two-year term with the possibility of renewal.

The potential start time for this position is Fall 2018/Winter 2019.


Application Material Required: 

Submit the following required application material online at this website by the October 31, 2018 deadline:
• Curriculum Vitae 

• Research Statement (up tp 3 pages)

• List of up to 10 most relevant publications, including contributions from multi-author publications

• We prefer at least three Reference Letters (to be submitted by the reference writers at this site)

SLAC National Accelerator Laboratory is an equal opportunity employer and welcomes nominations of women and minority group members. Hires at the SLAC National Accelerator Laboratory are subject to Department of Energy approval, and are required to complete Environmental Safety & Health training.

Further Info: Please see https://epp.slac.stanford.edu/ and https://pac.slac.stanford.edu/ or contact Dr. Michael Kagan (makagan@slac.stanford.edu), Dr. Phil Marshall (pjm@slac.stanford.edu), or Dr. Kazuhiro Terao (kterao@slac.stanford.edu) SLAC National Accelerator Laboratory 2575 Sand Hill Rd. Menlo Park, CA 94025


Application Materials Required:
Submit the following items online at this website to complete your application:
And anything else requested in the position description.

Further Info:
email
 
2575 Sand Hill Road, MS 95
Menlo Park, CA 94025

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