The Machine Learning (ML) Department Head in the Accelerator Research Division (ARD) is responsible for the overall intellectual leadership and management of the department, including providing administrative and technical direction to a team comprised of Research Associates, scientists and other technical experts. ARD carries out innovative R&D to support and enhance SLAC’s accelerator facilities (LCLS-II and FACET-II), to develop concepts for future accelerators, and to perform fundamental beam physics research. The ML department is responsible for developing and applying machine learning algorithms to automate control and analysis of large scale accelerator complex at SLAC. Applications include optimization of the FEL performance, processing electron and X-ray images, training surrogate models, virtual diagnostics, and statistical analysis of data. The successful candidate will work closely with accelerator research and operations, as well as with other machine learning groups at SLAC, in order to share resources and to develop ML algorithms, as well as to optimize accelerator operations.
Your specific responsibilities include:
Oversee development for automated tuning of LCLS-II and FACET-II
Implement tuning algorithms as part of LCLS-II commissioning and operations.
Translate machine learning concepts to accelerator operations.
Develop new ideas for solving high impact problems at LCLS-II and FACET-II with machine learning that improve user support.
Coordinate with other machine learning activities and initiatives in the lab.
Note: The Staff Scientist level is a regular-continuing position and requires a review and evaluation of documented scientific achievements. Applicants should include a cover letter, a statement of research 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 accelerator physics, high energy physics, or related field and minimum eight years progressively responsible experience in the following:
Leading, managing and mentoring scientist and technical staff with multi-disciplinary background.
Developing, evaluating and setting priorities for area of responsibility; including managing business, technical, and educational activities.
Real accelerator commissioning and operations.
Applying machine learning to accelerators.
Accelerator and FEL physics.
A back ground in computer science, mathematics and/or statistics a plus.
Demonstrated strengths in problem identification, independent decision making, accountability for outcomes, and collaborative problem solving.
In-depth understanding of a broad range of disciplines essential for particle accelerators.
Ability to develop and manage domestic and international collaborations, involving multi-disciplinary teams and multiple organizations.
Must have excellent verbal and written communication skills, both internally and also externally to funding agencies in DOE and elsewhere.
As one of 17 Department of Energy national labs, SLAC pushes the frontiers of human knowledge and drives discoveries that benefit humankind. We invent the tools that make those discoveries possible and share them with scientists all over the world.
What started as a group of 200 people, all focused on a single project – to build and operate the world’s longest linear accelerator – has grown over the last 50 years into a large and diverse workforce that performs and supports cutting-edge research across a variety of disciplines. Our 1,600 employees include scientists, engineers, technicians and specialists in a wide range of operational support areas, from human resources and business services to facilities, security and maintenance, all working together in a collaborative environment.
SLAC employs the best and brightest minds in their fields, and every member of our staff, working individually and in teams, makes important contributions to our success. By tapping into the interest and motivation of our employees and offering guidance and opportunities for development, we seek to provide an enriching work environment. As Stanford employees, SLAC staff members have the opportu...nity to partner with other world-class talent at one of the world’s best universities and can also take advantage of the many educational and social opportunities that Stanford offers.
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