Stanford University / SLAC National Accelerator Laboratory, FUNDAMENTAL PHYSICS DIRECTORATE

Position ID:Stanford University / SLAC National Accelerator Laboratory-FUNDAMENTAL PHYSICS DIRECTORATE-RAML [#14780]
Position Title: Research Assoc - Machine Learning Initiative
Position Type:Postdoctoral
Position Location:Menlo Park, California 94025, United States [map]
Subject Area: High Energy Physics
Appl Deadline:2019/10/31help popup (posted 2019/09/23)
Position Description:    

The SLAC National Accelerator Laboratory is seeking applicants for an experimental Research Associate (post-doc) position to work in the area of Machine Learning (ML) based event reconstruction for Neutrino Physics. SLAC is a member of the Deep Underground Neutrino Experiment (DUNE), MicroBooNE, and ICARUS collaborations, all of which will use Liquid Argon Time Projection Chambers (LArTPC) to detect neutrino interactions. DUNE, which will start beam operations in 2026, will have a 1300 km baseline and will provide the world's best measurements of neutrino oscillations. ICARUS, which will begin operations in early 2020, is a short-baseline experiment exploring anomalies observed in MiniBooNE and LSND, and measuring neutrino-Ar cross-sections. SLAC is actively involved in the simulation and reconstruction of events and will play a key role in the commissioning and neutrino oscillation analysis, the flagship measurement of the Short Baseline Neutrino (SBN) program.

LArTPCs record the trajectories of charged particles as 2D or 3D images. Recently, modern image analysis techniques borrowed from Computer Vision, in particular ML methods, have demonstrated great promise in realizing generic, high quality data reconstruction algorithms for LArTPC data. The SLAC group has led this effort in Neutrino Physics, in particular for LArTPC detectors.

Current SLAC involvement in ICARUS and DUNE

  • ML Based Data Reconstruction Techniques R&D for LArTPCs
  • Cross-section and Oscillation Physics Analysis
  • Pixel-based LArTPC R&D for the DUNE near detector
  • High-Bandwidth Data Acquisition Systems
  • Cryogenic Electronics

The successful candidate will be expected to take on significant responsibility in ML-based data reconstruction algorithm development as well as leading physics data analysis for the DUNE and SBN programs. We are specifically looking for expertise in machine learning and computer vision techniques and/or scalable software development for High Performance Computing (HPC) clusters. ML methods currently being worked on at SLAC include convolutional neural network for image feature extraction, graph neural network for object correlation reconstruction, and generative models in simulation as well as mitigating domain discrepancies (e.g. data vs. simulation). The neutrino group collaborates closely with other groups across HEP (e.g. ATLAS/LSST) and beyond (accelerator R&D, photon science, biology) for interdisciplinary ML technique development, which is realized through the SLAC lab-wide ML initiative effort. We support preparation for possible future careers in both the experimental neutrino physics and machine learning applications for physics in general. The initial term guarantees 24 months of employment, and a further yearly extension may be negotiated at the end of each term (up to a maximum of five years). The start date is negotiable, however the preference is from an immediate start date to early summer 2020. For further information, please contact Kazu Terao (kterao@slac.stanford.edu)

Your specific responsibilities include:

  • Work with the team of graduate students, post-docs, and scientists to develop and apply ML methods to high-impact. science.
  • Work with the lab-wide ML initiatives effort to develop ML-based research solutions.
  • Publish original physics results and/or ML methods for physical science.

Qualifications

  • Primarily a Ph.D. in experimental particle physics, but also other relevant areas will be considered including computer science, statistics, and other fields of physics.
  • Strong background in software and algorithm development. Experience in scalable software for distributed GPU computing will be highly valued.
  • Ability to carry out independent research.
  • Strong analytical and experimental skills.
  • Effective written and verbal communication skills.
  • Ability to work and communicate effectively with a diverse population.

How to apply:

Interested candidates should submit the following:

  • A letter of application indicating the primary physics program of interest (Neutrino Physics).
  • C.V.
  • Selected bibliography that highlights personal contributions.
  • Brief statement of research interest.
  • At least three letters of recommendation from senior researchers who know the candidate's work.

Applications will be accepted until the position is filled but must be received by October 31st,  2019 to ensure full consideration.


SLAC National Accelerator Laboratory is an Affirmative Action / Equal Opportunity Employer and supports diversity in the workplace. All employment decisions are made without regard to race, color, religion, sex, national origin, age, disability, veteran status, marital or family status, sexual orientation, gender identity, or genetic information. All staff at SLAC National Accelerator Laboratory must be able to demonstrate the legal right to work in the United States. SLAC is an E-Verify employer.


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:
https://sites.slac.stanford.edu/neutrino/research/algorithms-machine-learning
email
 
2575 Sand Hill Road, MS 95
Menlo Park, CA 94025

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