SLAC National Accelerator Laboratory

SLAC Job Postings

Position Overview:

Stanford Synchrotron Radiation Lightsource (SSRL), a SLAC National Accelerator Laboratory Science Directorate at Stanford University, is a national scientific user facility. SLAC has an active program in the field of Microelectronics through its active participation in several initiatives related to the Department of Energy. The following are the expectations of the job. The summer internship will consist of developing AI-guided automation tools to enable autonomous x-ray characterization of thin films relevant to non-volatile memory. The intern should have a good grasp of science concepts and some background in programming, preferably in Python.

The student will work closely with members of a team involved in the growth and characterization of new classes of ferroelectric and oxide semiconducting materials towards the end goal of lower energy consumption electronic devices. One of the primary characterization techniques used will be synchrotron x-ray diffraction (XRD) carried out at SSRL. This project would involve creating a system where the results of XRD analysis are used to adjust parameters of the XRD experiment. For instance, if the analysis reveals a weak signal, the system could automatically increase the exposure time or adjust the sample position. This would require an interface between the data analysis software and the control software of the XRD instrument. Students could design a basic control loop where the software would make an initial measurement, analyze the data, and then adjust parameters and repeat the measurement based on the initial analysis.

In this position the student will have the opportunity to gain valuable hands-on experience by working to bridge their knowledge of basic physics principles with new perspectives in thinking about using ML algorithms towards solving real world problems and gain work experience.

Specific responsibilities (include but are not limited to):

• Conduct data analysis to ascertain the quality and verity of data, in partnership with the mentor.
• Feature extraction from data to guide next experimental steps
• Develop machine learning (ML) models to predict the next data collection step
• Program the bluesky data acquisition system to automate data collection
• Prepare reports and scientific publications outlining the advances under the aegis of the mentor.

Opportunities and Benefits

• Growth and mentorship from exceptionally talented engineers and scientists from SLAC and Stanford University.
• A mission-driven, stable, collaborative, highly interdisciplinary, and supportive work environment.
• Opportunity to experience a multidisciplinary research environment, integrating knowledge from many subject areas spanning computer engineering, physical sciences, applied mathematics, and software applications.

Note: This is an hourly, non-benefits eligible temporary-nonexempt, internship position (work at 50% full-time equivalent or more), not to exceed 980 hours in six consecutive months. Eligible applicants must be at least 18 years of age, currently enrolled in an educational program or recently graduated, and have US work authorization. The on-site internship program is for a period of eight weeks and takes place between June and Mid-August, with the start date being contingent on the convenience of the candidate. Flexibility in making the position 100% remote, hybrid or fully onsite.

To be successful in this position, candidates should:

• Pursuing a Bachelor¿s or Master's degree in a science, engineering or equivalent discipline.
• Some prior experience at Python programming would be beneficial.
• Strong communication skills.
• Ability to work in a collaborative environment.
• Passionate about innovative solutions for Science & Engineering problems.

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

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: SLAC Intern Students (Level III)
• Job Code: 0901
• Duration: Temporary

The expected pay range for this position is $26.68 to $38.11 per hour. 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.