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.
SLAC manager competencies:
- Results Through Others: Achieves expected results by effectively delegating and managing the work of others.
- Aligns Priorities: Ensures planning and prioritization of resources and work efforts; ensures alignment of direct and matrix reports to support organizational goals and business plans.
- Applies Lab Acumen: Maintains understanding of lab efforts and direction as well as current research and trends, considers technology and customer impacts, and contributes relevant, informed ideas to lab growth.
- Navigates Complexity: Demonstrates effective problem-solving and decision-making in complex situations; manages a multitude of information and circumstances to discern what is most important; takes appropriate action, even with conflicting data or in difficult situations.
- 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.
- Self-awareness: Seeks feedback from others and takes ownership of, and actions to address what is learned; recognizes impact on others and adjusts as needed; pursues continuous learning opportunities; implements a meaningful development plan.
- Team Effectiveness: Effectively motivates team members and fosters a diverse and collaborative environment; leverages individual members’ strengths for overall team effectiveness; incorporates insights to improve team operations.
- Purpose & Vision: Articulates a clear vision of expected outcomes; inspires others to execute work plans and feel a sense of purpose and ownership for the mission.
- Attracts & Develops Employee Talent: Plans for, attracts, and hires the right talent for current and future organizational needs; operates with a focus on growing internal talent through organizational and staff development; values and encourages continuous growth development through a blend of work experiences, coaching, and formal learning; aligns individual development with organizational needs and objectives.
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.
- 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.
To apply for this job please visit chu.tbe.taleo.net.