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
- ML Research Associate ... deadline 1/31/2022
- Link for details + how to apply
SLAC National Accelerator Laboratory seeks research associate with a proven track record of recognized scientific achievement in applying machine learning (ML) to the physical sciences. SLAC is one of the world’s premier research laboratories, with internationally leading capabilities in photon science, accelerator physics, high energy physics (HEP), and energy sciences. Machine learning is expected to play an important role in nearly every major project at SLAC. Applications include deep learning for detector analysis, online control of facilities, surrogate models for high-fidelity simulations, and new modes of data analysis to handle data rates that can reach TBs/second. Though still in its early stages in physics, ML is expected to have a transformative effect on SLAC’s science output.
The new ML initiative was founded to push the limits of SLAC’s highest impact 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 unsolved problems in science. This position will focus on applications of uncertainty quantification and solving inverse problems in photon scienceFor the RA position, candidates must have graduate-level experience in applied machine learning.