The following machine-learning focused research positions are now open at SLAC. Postdoc/RA positions typically start with a two-year contract followed by a yearly renewal of a contract. A project scientist is a fixed-term position without a guaranteed promotion review. An associate scientist is also a fixed-term position with a promotion review at the end of the term. A full staff scientist position has no contract termination date.

 

ML Postdoc Positions

  • Neutrino Physics (DUNE/SBN) ... deadline 10/31/2019
    • Link for details + how to apply
    • Brief description: develop a full physics data reconstruction chain for hi-resolution particle imaging detectors, study data vs. simulation discrepancies, and minimize the impact of systematic uncertainties using machine learning methods. Knowledge in particle physics, software skills in pytohn/pytorch/tensorflow, and experience in CNN/GNN/Generative Models are highly valued.
  • ML Research Associate ... deadline 12/31/2019
    • Link for details + how to apply
    • Researchers in the ML initiative work hand-in-hand with domain scientists applying ML methods to control facilities, scale analysis to PB data sets, develop new types of analysis, and generally discover new science. Though the position is not oriented towards foundational computer science, the candidate should be comfortable enough with ML to extend the limits of current algorithms.  Likewise researchers should have sufficient science background to be an integral member of domain science teams. The role is interdisciplinary and collaborative, and the candidate will work jointly with scientists and engineers at SLAC, academics at Stanford, industrial partners in Silicon Valley, and facility users from around the world. Finally, the candidate should have a creative spark to tackle challenging, unsolved problems in science. For the RA position, candidates should have graduate-level experience in applied machine learning.

 

ML Scientist Positions

  • ML Associate Staff Scientist ... deadline 12/31/2019
    • Link for details + how to apply
    • Researchers in the ML initiative work hand-in-hand with domain scientists applying ML methods to control facilities, scale analysis to PB data sets, develop new types of analysis, and generally discover new science. Though the position is not oriented towards foundational computer science, the candidate should be comfortable enough with ML to extend the limits of current algorithms.  Likewise researchers should have sufficient science background to be an integral member of domain science teams. The role is interdisciplinary and collaborative, and the candidate will work jointly with scientists and engineers at SLAC, academics at Stanford, industrial partners in Silicon Valley, and facility users from around the world. Finally, the candidate should have a creative spark to tackle challenging, unsolved problems in science. For the staff scientist position, candidates should have post-doctoral experience in applied machine learning.