The Particle Physics & Astrophysics (PPA) Department at SLAC National Accelerator Laboratory/Stanford University seeks applicants in an open-ranked search (tenure-track assistant professor to associate or full for exceptional candidates) in any area of research pursued within SLAC’s growing program in Quantum Information Sciences (QIS) or Machine Learning/Artificial Intelligence (ML/AI). As described below, SLAC’s programs in QIS/ML/AI span the full spectrum of active research at the laboratory, which includes both high-energy physics (PPA) and photon science (PS). Therefore, depending on the qualifications and interests of the candidate, a joint appointment with the Photon Science Department may also be of interest and could be considered, even though PPA is leading this particular search. The successful applicant should demonstrate the potential to lead an outstanding research effort that builds on, enhances, or expands the scope of our existing efforts in QIS or ML/AI. 

In the area of QIS, SLAC is a partner in the Q-NEXT national quantum center, which includes hosting the SLAC Detector Microfabrication Facility, a premier foundries specializing in the fabrication of quantum devices (see: https://www6.slac.stanford.edu/research/new-technologies). With DOE Office of Science support, PPA and PS faculty, together with Stanford University faculty, are conducting QIS research in the area of quantum simulations, quantum sensors and transducers, and are devising new experiments that will use these technologies. In the area of ML/AI, SLAC has a vibrant ecosystem of pioneering machine learning applications across experimental and theoretical particle physics, astrophysics and cosmology.  SLAC is host to several data-intensive scientific discovery facilities (LSST and LCLS-II) that are challenging the boundaries of data-driven science discovery; operates the world’s most powerful X-ray Free Electron Laser (LCLS-II), which itself presents unique and challenging opportunities for ML in maintaining reliable real-time optimal beam delivery from a complex accelerator and beamline system and works on these kinds of problems in close partnership with the Computer Science and other departments at Stanford. SLAC is also a major partner in ML efforts at data-intensive particle physics experiments (e.g. ATLAS at the CERN’s LHC and DUNE at Fermilab’s LBNF). We welcome candidates who would bring additional dimensions or directions to our program. Additionally, candidates should possess good communication skills and a commitment to effective mentoring.

SLAC and Stanford University value faculty who are committed to advancing diversity, equity, and inclusion. Candidates may optionally include as part of their research statement a brief discussion of how their work will further these ideals.

All applicants must have a Ph.D. in Physics or related subject field and should send materials to the search committee through AcademicJobsOnline. Candidates should upload a cover letter, curriculum vitae (including a publication list), a research statement as well as a brief (< 300 word) summary of their expected research program (three-page combined maximum). We encourage applicants, as a component of their research statement, to discuss their experience and approach to mentoring. At least three and no more than five letters of reference should be submitted as part of the application.

Review of applications will begin on February 1, 2024; later applications may be considered at the discretion of the search committee.

Stanford is an equal employment opportunity and affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law. Stanford welcomes applications from all who would bring additional dimensions to the University's research, teaching and clinical missions.

The expected base pay range for this position is $182,557-$347,053 per annum, based on 12 month salary.