Job Information
SLAC National Accelerator Laboratory Research Associate - Machine Learning and Material Science (LCLS/LDRD) in Menlo Park, California
Research Associate - Machine Learning and Material Science (LCLS/LDRD)
Job ID
6040
Location
SLAC - Menlo Park, CA
Full-Time
Temporary
SLAC Job Postings
Position overview:
SLAC National Accelerator Laboratory is seeking a Research Associate with a proven track record of scientific achievement with relevant experience in material science, photoemission, ultrafast x-ray and laser sciences as well as in machine learning (ML) techniques.
In this position, you will be supporting a research effort managed by the Linac Coherent Light Source (LCLS) and Laboratory Directed Research and Development (LDRD) program. You will focus on developing a model to enable end-to-end alignment of electron probes that are integrated with 1-MHz LCLS-II. This will expand the horizon of complex photoelectron spectrometers and capabilities. Multi-lens configurable electron optics coupled with a state-of-the-art free electron laser will allow the exploration of exotic quantum materials, nanomaterials, and complex gas phase systems. To reduce the time for alignment in this system, an end-to-end machine learning-directed model will be developed to automate the alignment of the electron optics. Efforts here focus on a specific use case at LCLS-II. The overall aim is to build digital twins of the spectrometers. This will consist of a high-fidelity multi-physics model capable of describing the micro and macro features of the electron imaging lens. Mirroring the state of and behavior of the physical system is a key goal of this project. The research associate will also participate and expected to lead experiments at facilities to demonstrate the developed models and capability.
Your specific duties include:
Develop machine learning-directed model to automate the alignment of the electron optics.
Conduct photoemission and photoelectron microscopy experiments and analysis of measurements acquired from x-ray beam times on materials at synchrotron and x-ray facilities.
Use machine learning methods to develop a digital twin of the microscope
Apply advanced optimization techniques to the alignment task, including Bayesian optimization and related methods.
Analyze x-ray scattering, photoemission, and imaging measurements.
Note : The Research Associate role is a fixed term staff position. Appointment duration is 24 months, 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 curriculum vitae which includes a list of publications, and names of three references for future letters of recommendation with the application.
Preview of applications begins immediately. Applications are accepted until the position is filled. Interested candidates should submit a cover letter, CV, and three references.
To be successful in this position you will bring:
Ph.D. in physical sciences (physics, chemistry, material science), quantum information, mathematics, computer science, computational science or engineering.
Experience in theoretical computations, machine learning, big data analysis.
Experience with programming (Python, PyTorch/TensorFlow, C++, etc)
Willingness to learn how to use open-source modeling platforms, analysis of big data.
Ability to carry out independent and collaborative research in a diverse research team.
Effective written and verbal communication skills. good interpersonal skills are essential.
Desired experience:
Experience developing in autodiff languages (PyTorch, TensorFlow, JAX, Julia)
Experience applying Bayesian optimization to science tasks
Background in photon science and/or accelerator physics
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 will require onsite work.
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: https://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 Assoc-Experimental
Grade : G, Job code: 0127
Employment Duration : Fixed term – 24 months
The expected pay range for this position is $70,000 - $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.