Job Title: Associate Scientist - Machine Learning for Accelerators

SLAC Job Postings

Position Overview:

The Accelerator Research Division (ARD) at the SLAC National Accelerator Laboratory seeks a scientist with a proven track record in applying novel machine learning approaches to modeling and control of particle accelerators.

The ARD Machine Learning Department performs research and development on the use of machine learning to address modeling, optimization, and data analysis problems in particle accelerators. We seek a candidate with experience in developing and deploying machine learning algorithms in a computational and/or experimental setting, as well as experience in accelerator physics. The candidate must be proficient in python.

Machine learning is playing an increasing role in helping to enable unprecedented capabilities in modeling and control of complex, nonlinear particle beam dynamics in particle accelerators (which in turn enables new scientific capabilities). The position would be centered around developing new solutions for accelerator modeling and control. Specific areas of focus would depend on the interests of the candidate and could range from algorithm development (e.g. physics-informed ML, combining classical computational techniques with ML), adapting existing techniques to challenging new beam setups, developing new computational techniques for ML-enhanced accelerator simulations (e.g. differentiable simulations and ML), and integrating online modeling and tuning solutions into regular operation. Opportunities also exist to be involved in the entire development cycle from algorithm design to online deployment, and across different facilities at SLAC and collaborating facilities at other national labs.

SLAC is one of the world's premier research laboratories, with capabilities in photon science, accelerator physics, high energy physics, and energy sciences. More information can be found on SLAC's website: https://www6.slac.stanford.edu/ . SLAC houses accelerators that produce beams at the edge of current state-of-the-art, including LCLS, LCLS-II and FACET-II. Beams at these facilities are able to be highly customized in 6D position-momentum phase space and must be tailored to each scientific use-case. These machines support exciting science in biology, chemistry, material science, novel acceleration technologies (e.g. plasma-based acceleration techniques), and the physics of "extreme" particle beams (e.g. high-intensity, short, high-charge beams and their control). SLAC also houses the SPEAR3 accelerator that provides light for users at the Stanford Synchrotron Radiation Lightsource (SSRL), and the SLAC Megaelectronvolt Ultrafast Electron Diffraction Instrument (MeV UED).

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

Your specific responsibilities include : Developing, testing, and deploying novel machine learning based solutions to challenging problems in accelerators. This could also involve addressing computational challenges (e.g. ML-enhanced simulations). Through the above, contributing to the scientific goals of LCLS-II (superconducting and normal-conducting linacs) and FACET-II. Contributing to open-source community code development and publications. Mentoring junior scientists (e.g. graduate students, postdoctoral scholars). Contributing to development and writing of funding proposals. Helping to integrate ML algorithms into regular operations for accelerators. Note : The Associate Scientist position is the entry level in the Staff Scientist career path. The Associate Scientist is a 3 to 5 year fixed term research staff position. The second level of this career path, Staff Scientist is a regular continuing position. The Associate position is scheduled to be assessed after the 3 to 5 year period with the possibility of appointment to the Staff Scientist level. Appointment to the Staff Scientist level requires a review and evaluation of documented scientific achievement.

To be successful in this position you will bring: Ph.D. in Accelerator Physics, Physics, Electrical Engineering, Computer Science or related field and two years of experience in the following: Leading research efforts in machine learning applied to particle accelerators. Using accelerator physics simulation codes. Writing and deploying software in an accelerator control system environment. Accelerator diagnostic hardware and beam characterization. Developing and delivering research activities, including progress reports, proposals, requirements documentation, and presentations to diverse stakeholders. Performing experiments in complex user facility and/or R&D environment. Proficiency in python (experience with additional codes, such as Julia, Lua, MATLAB is a plus). Proven track record of high-impact research in the area of accelerator physics (and ideally in machine learning). Research record as evidenced by a publication record commensurate with level of work experience. Interest in and commitment to developing community-driven, open-source code bases for ML in accelerators. The ability to carry out research, collaborate closely with colleagues conducting research, and participate in the writing of scientific proposals to fund research. Excellent verbal and written communication skills and the ability to effectively conveying complex technical concepts. Ability to work and communicate effectively with a diverse population. Ability to collaborate across organizations and manage/lead cross-functional efforts. In addition, preferred requirements include: Experience working with EPICS-based control systems. Experience with both computational and experimental aspects of accelerator modeling and control (e.g. running simulations and carrying out experiments on an accelerator). Proven track record of open-source community code development and deployment. SLAC employee competencies : Effective Decisions : Uses job knowledge and solid judgment to make quality decisions in a timely manner. Self-Development : Pursues a variety of venues and opportunities to continue learning and developing. Dependability : Can be counted on to deliver results with a sense of personal responsibility for expected outcomes. Initiative : Pursues work and interactions proactively with optimism, positive energy, and motivation to move things forward. Adaptability : Flexes as needed when change occurs, maintains an open outlook while adjusting and accommodating changes. Communication : Ensures effective information flow to various audiences and creates and delivers clear, appropriate written, spoken, presented messages. Relationships : Builds relationships to foster trust, collaboration, and a positive climate to achieve common goals. Physical requirements and working conditions: Consistent with its obligations under the law, the University will provide reasonable accommodation to any employee with a disability who requires accommodation to perform the essential functions of the job. Work Standards : Interpersonal Skills: Demonstrates the ability to work well with Stanford colleagues and clients and with external organizations. Promote Culture of Safety: Demonstrates commitment to personal responsibility and value for environment, safety and security; communicates related concerns; uses and promotes safe behaviors based on training and lessons learned. Meets the applicable roles and responsibilities as described in the ESH Manual, Chapter 1-General Policy and Responsibilities: http://www-group.slac.stanford.edu/esh/eshmanual/pdfs/ESHch01.pdf Subject to and expected to comply with all applicable University policies and procedures, including but not limited to the personnel policies and other policies found in the University's Administrative Guide, http://adminguide.stanford.edu ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Classification Title: Associate Scientist

Grade: K

Job code: 1092

Duration: Fixed Term

The expected pay range for this position is $89,000 to $171,000 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.