Research Associate - Experimental (Machine Learning)

SLAC Job Postings

SLAC National Accelerator Laboratory seeks a research associate with a proven track record of recognized scientific achievement in applying machine learning (ML) to the physical sciences. SLAC is one of the world's premier research laboratories, with internationally leading capabilities in photon science, accelerator physics, high energy physics (HEP), and energy sciences.

Machine learning is expected to play an important role in nearly every major project at SLAC. Applications include deep learning for detector analysis, online control of facilities, surrogate models for high-fidelity simulations, and new modes of data analysis to handle data rates that can reach TBs/second.

The ML department's goal is to support discovery across SLAC's science mission. Though the position is not oriented towards foundational computer science, the candidate should be comfortable enough with ML to extend the limits of current algorithms. Likewise researchers should have sufficient science background to be an integral member of domain science teams. The role is interdisciplinary and collaborative, and the candidate will work jointly with scientists and engineers at SLAC, academics at Stanford, industrial partners in Silicon Valley, and facility users from around the world. Finally, the candidate should have a creative spark to tackle unsolved problems in science.

This position will be primarily targeted at developments supporting experimental design tasks. Research experience in Bayesian experimental design/active learning and uncertainty quantification will be considered a plus. The ideal candidate will also have a demonstrated track record in building and deploying scientific software tools and show expertise in applying a diverse set of machine learning models.

The applicant can expect to work on projects across the range of science domains at SLAC, including accelerator physics, materials science, and structural biology.

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

Your specific responsibilities include:

• Work with domain science teams to enable high-impact science through ML.
• Publish original research on ML applications and ML-enabled advances in science.
• Develop new projects applying ML to high-impact science at SLAC.
• Topics of particular interest include experimental design and uncertainty quantification

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 including a brief summary of accomplishments, a curriculum vitae, a list of publications, and the names of three references for future letters of recommendation with the application.

We are looking for candidates, with the following criteria in mind:

• Ph.D. in Computer Science, Physics, Chemistry, Materials Science, Computational Biology, or related field, including research in applied machine learning.
• Strong background in the physical sciences, ideally at the graduate level, and strong interest in fundamental research in the physical sciences.
• Strong background in machine learning and experience in collaborative software projects.
• Research/publication record commensurate with level of work experience.
• Experience carrying out novel research, collaborating closely with colleagues conducting research.
• Excellent verbal and written communication skills and the ability to convey complex technical concepts.
• Ability to work and communicate effectively with a diverse population.

In addition, preferred requirements include:

• Experience (2+ years) in accelerator physics, HEP, structural biology, pharmaceutical drug design, or materials science
• Experience (2+ years) in Bayesian experimental design

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. May work extended hours during peak business cycles.

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

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Classification Title: Research Associate - Experimental
Grade: G
Job code: 0127
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.