Job Title: Research Associate - AD ARD Machine Learning

SLAC Job Postings

Position Overview:

The Machine Learning Department in the Accelerator Research Division at SLAC National Accelerator Laboratory is seeking to fill several open positions to work with our team on AI/ML solutions for challenging problems in modeling and control of particle accelerators. 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, 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). SLAC also collaborates heavily with other laboratories on Machine Learning and cross-facility algorithm transfer and community software development.

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

Your specific job responsibilities include: Developing, testing, and deploying novel machine learning based solutions to challenging problems in accelerators. Specific area of focus will depend on the candidate's interests and needs of the current research programs; these can range from addressing primarily theoretical/ computational challenges (e.g. ML-enhanced simulations) to primarily experimental ones (e.g. developing and testing new tuning algorithms for high-impact experiments). Data gathering, data analysis, and code development as needed for individual applications. Through the above, contribute to the scientific goals of LCLS-II (superconducting and normal-conducting linacs) and FACET-II. Contribute to papers and reports on the research being conducted; participate in conferences and other avenues for sharing research results. Contribute to and develop community-driven open-source code bases for machine learning in accelerators (e.g. see Xopt, LUME currently under development: https://github.com/ChristopherMayes/Xopt , https://www.lume.science/ ). Note: The Research Associate role is a fixed term staff position. This is a 2 year fixed-term appointment with the possibility of extension. Assignment duration is contingent upon project needs and funding.

Applicants must provide evidence of either a recently completed PhD degree or confirmation of completion of the PhD degree requirements prior to starting the position. Applicants should also include a cover letter, a statement of research area including brief summary of accomplishments, a curriculum vitae, a list of publications, and names of three references for future letters of recommendation with the application.

To be successful in this position you will bring: Ph.D. in physics, applied physics, engineering, computer science, or related fields, and coursework or/and research experience in the following areas: machine learning/ artificial intelligence optimization, control systems, or related topics Demonstrated knowledge with programming in python Strong experimental, analytical and computation skills Effective written and verbal communication skills Ability to work and communicate effectively with a diverse population Ability to work both independently and within a team environment In addition, preferred requirements include: Familiarity with accelerators/accelerator physics in some capacity (e.g. experimental, theoretical) Experience with a variety of programming codes (e.g. Julia, MATLAB, python) Familiarity with EPICS and/or other accelerator control systems 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, team 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 his or her 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: Research Associate-Experimental Grade: G Job code: 0127 Duration: Fixed Term The expected pay range for this position is $54,000 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.