Job Description

Research Associate - Machine Learning for Accelerators Job ID 6481 Location SLAC - Menlo Park, CA Full-Time Temporary **SLAC Job Postings** **Position Overview:** The Machine Learning Department in the Accelerator Research Division at SLAC National Accelerator Laboratory is seeking to fill open positions to work with our team on AI/ML solutions for challenging problems in modeling and control of particle accelerators. AI/ML is playing an increasing role in helping to enable unprecedented capabilities in modeling and control of complex, nonlinear systems 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, reinforcement learning algorithms for accelerator control, adaptive modeling), 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 / digital twins and optimization / control solutions into regular accelerator operation. Opportunities also exist to be involved in the entire development cycle from algorithm design to software development to online deployment, or focus on specific parts of this cycle. Work is conducted across different facilities at SLAC and collaborating facilities at other national labs. Our team also works on advanced computational workflows linking high performance computing (HPC) with experiments in real-time, in collaboration with other groups across SLAC, NERSC, and other national laboratories more broadly; this work helps enable AI/ML-based experiment guiding and digital twins for accelerators. Our team is heavily oriented toward collaboration with the broader accelerator community and works collaboratively with many groups across SLAC. Particle accelerators also involve numerous areas of interest for AI/ML research; these include, for example improving generalization to out-of-distribution data, adaptive compensation for time-varying changes in accelerator behavior, continual learning, sim2real for complex systems, uncertainty quantification, physics-informed and physics-constrained learning, etc. SLAC is one of the world¿s premier research laboratories, with capabilities in photon science, accelerator physics, high energy physics, energy sciences, and increasingly AI/ML for science. More information can be found on SLAC¿s website: . 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.** **Hybrid or on-site is preferred.** **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 physics simulations) to primarily experimental ones (e.g. developing and testing new tuning algorithms for high-impact experiments, for example building on reinforcement learning, Bayesian optimization, and model predictive control). + 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), FACET-II, and other accelerators at SLAC and beyond. + 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, Badger, LUME, and Cheetah currently under development: , , ). **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 + Basic experience programming in python + Strong experimental, analytical and computation skills + Effective written and verbal communication skills + Ability to work and communicate effectively with individuals from different technical background (engineers, scientists, technicians) + Ability to work both independently and within a team environment + Strong sense of curiosity, desire to learn, and ability to take initiative in research projects **Preferred (but not strictly required) qualifications include:** + Past experience with both particle accelerators and machine learning, or similar mix of science and ML experience + Experience with Pytorch and/or other ML-oriented python libraries **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 their job. **Work standards** : + Interpersonal Skills: Demonstrates the ability to work well with Stanford/SLAC 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: + 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, ___________________________________ + Classification Title: Research Associate - Experimental + Job code: 0127 + Grade: G + Employment Duration: Fixed Term _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_ . SLAC National Accelerator Laboratory is an Affirmative Action / Equal Opportunity Employer and supports diversity in the workplace. All employment decisions are made without regard to race, color, religion, sex, national origin, age, disability, veteran status, marital or family status, sexual orientation, gender identity, or genetic information. All staff at SLAC National Accelerator Laboratory must be able to demonstrate the legal right to work in the United States. SLAC is an E-Verify employer.

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Full time, Temporary work, Fixed term contract, Flexible hours,

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