The SLAC National Accelerator Laboratory is seeking applicants for an experimental Research Associate (post-doc) position to work in the area of High Energy Physics (HEP), in particular experimental neutrino physics, and Artificial Intelligence and Machine Learning (AI/ML) research.

About us:
The Machine Learning Initiatives (MLI) is an organization dedicated to support R&D of AI/ML for science at SLAC. In the last five years, MLI established an exciting AI/ML research environment at the Lab with opportunities to interact and collaborate with AI/ML experts with diverse science backgrounds. These collaborators come from all science directorates at the Lab as well as Stanford campus and collaborating institutions at other universities and national labs. AI/ML research topics include physics inference and statistical analysis, design optimization, facility control and operations, and more.

The SLAC Neutrino group is a member of key neutrino experiments in the U.S. including the Deep Underground Neutrino Experiment (DUNE) and the Short Baseline Neutrino program (SBN). The group has led AI/ML research in the international neutrino community including on-going collaboration with the neutrino physics community in Japan. Particular areas of research include deep learning applications for data reconstruction and physics inference, differentiable physics simulation (DiffSim), uncertainty quantification (UQ), and R&D of scientific foundation models (FM). 

About the position:
In 2024, the MLI and Neutrino groups will launch a new organization: Center for Artificial Intelligence for Nu physics (AINU). We are searching for up to two research associates (RAs) to take core responsibilities in AINU. The goal of AINU is to accelerate and expand the impact of AI/ML for neutrino physics through the activities listed below:

1. R&D of DiffSim, UQ, and FM for neutrino physics research
2. R&D and operation/hosting of a large-scale public data portal for neutrino experiments
3. Events utilizing the data portal including AI/ML schools, data olympics, and workshops
4. Visitors's program where collaborators from outside of SLAC  are invited to develop proof-of-principle (incubation) in AI/ML research

A successful candidate will be offered an opportunity to pursue research in one or two topics in DiffSim/UQ/FM with neutrino physics dataset.  Furthermore, the candidate will be expected to take major responsibilities in launching the AINU, working with the members of the MLI, Neutrino, as well as groups from the Stanford physics and computer science departments. Computing resources available for this work include local GPU clusters with NVIDIA GPUs (28 A100, 280 RTX 2080Ti), current allocation at the NERSC Perlmutter (A100 cluster), and other potential HPC centers where we apply for future allocations. The storage for the public dataset is aimed at 1 petabyte.

We support preparation for possible future careers in both experimental neutrino physics and machine learning applications for physics in general. The initial term for the appointment is 24 months, with the possibility of yearly extensions up to a maximum of five years. For further information, please contact Kazu Terao (kterao@slac.stanford.edu).

Given the nature of this position, SLAC is open to on-site and hybrid work options.

Qualifications

• Ph.D.in experimental particle physics, computer science, or related fields. Expertise in neutrino physics is preferred. Experienced candidates including other fields of physics will also be considered.
• Knowledge in statistics, data analysis, algorithms and software development will be required. Strong background in AI/ML, in particular FM/UQ/DiffSim, will be highly valued.
• Ability to carry out independent research.
• Effective written and verbal communication skills.
• Ability to work and communicate effectively with a diverse population.

Interested candidates should submit the following:

• C.V.
• Selected bibliography that highlights personal contributions
• Research statement that describes your past experience, research interest, and goals
• Candidates should also ask at least three senior researchers who know their work to provide letters of recommendation.

The expected pay range for this position is $70,000 to $100,000 per annum. SLAC National Accelerator Laboratory/Stanford University provides pay ranges representing its good faith estimate of what the university reasonably expects to pay for a position. The pay offered to a selected candidate will be determined based on factors such as (but not limited to) the scope and responsibilities of the position, the qualifications of the selected candidate, departmental budget availability, internal equity, geographic location and external market pay for comparable jobs.