SLAC National Accelerator Laboratory
Research Associate - Theoretical X-ray Spectroscopy
SLAC National Accelerator Laboratory, Menlo Park, California, United States, 94029
Research Associate – Theoretical X‑ray Spectroscopy
Posted 6 days ago. Be among the first 25 applicants.
Position Overview
The Chemistry and Catalysis Division at the Stanford Synchrotron Radiation Lightsource (SSRL), SLAC National Accelerator Laboratory invites applications for a Research Associate position focused on computational and operando spectroscopy theory for catalytic and electrochemical systems. This role combines advanced electronic‑structure simulations, workflow development, and data‑centric analysis to interpret operando X‑ray absorption spectroscopy (XAS) data and bridge theory with experiment.
The successful candidate will develop and apply first‑principles methods to model and interpret X‑ray absorption spectra of catalytic and electrochemical systems, including batteries, under realistic working conditions. A key aspect of this role will be the creation of integrated computational‑experimental workflows that convert raw experimental data into AI‑ready physical descriptors and interpretable insights that can ultimately support foundational and generative modeling approaches.
This position offers a unique opportunity to contribute to the multi‑laboratory ISAAC initiative, one of whose goals is to transform X‑ray absorption spectroscopy into a data‑driven science where simulations, experiments, and physics‑informed AI collectively advance our understanding of dynamic materials and interfaces.
Note: The Research Associate role is a fixed‑term staff position. This 2‑year fixed‑term position may be extended depending on project needs and funding.
Applicants must provide evidence of a completed PhD degree or confirmation of completion prior to starting. Include a cover letter, a statement of research area with brief summary of accomplishments, a curriculum vitae, a list of publications, and names of at least three references. SLAC is a DOE laboratory operated by Stanford University in Menlo Park, CA.
Your Specific Responsibilities Include
Perform first‑principles simulations (DFT, TDDFT, BSE) of X‑ray absorption spectra for catalysts and electrochemical interfaces under realistic environments.
Optimize extended systems and surfaces, model adsorbates and reaction intermediates, and compute thermodynamic descriptors relevant to catalysis and battery materials.
Develop and automate computational workflows on high‑performance computing platforms (NERSC, S3DF, Sherlock, etc.) for large‑scale spectral and structural modeling.
Integrate theoretical modeling with operando experimental data to extract interpretable, uncertainty‑calibrated descriptors suitable for AI‑ready databases.
Investigate the use of machine‑learning tools for physics‑informed insight extraction from simulated and experimental datasets.
Collaborate closely with experimental scientists to interpret high‑throughput operando X‑ray data and guide the design of new measurements.
Publish research findings in high‑impact journals and present results at major scientific meetings.
To Be Successful In This Position You Will Bring
Ph.D. in Physics, Chemistry, Materials Science, Chemical Engineering, or a related field.
Expertise in first‑principles calculations (DFT, TDDFT) and electronic‑structure theory.
Demonstrated experience with operando X‑ray absorption spectroscopy interpretation.
Demonstrated ability to model extended systems, surfaces, and adsorbates under electrochemical or catalytic conditions.
Experience optimizing computational workflows and using HPC facilities for large‑scale simulations.
Strong programming skills (Python, C++, or MATLAB) for data handling and workflow automation.
Proven publication record in computational spectroscopy, catalysis, or materials modeling.
In Addition, Preferred Requirements Include
Familiarity with high‑throughput or workflow‑based simulation environments and data formats compatible with AI/ML pipelines.
Experience integrating theoretical and experimental datasets into structured, reusable, and AI‑ready repositories.
Understanding of uncertainty quantification and physics‑informed modeling for spectroscopy.
Motivation to explore hybrid approaches that combine physical theory, machine learning, and generative AI.
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.
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 this job.
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.
Subject to and expected to comply with all applicable University policies and procedures.
Title Classification: Research Associate.
Job Code: 0127, Grade G.
Duration: 2‑year fixed term.
The expected pay range for this position is $70,000 – $100,000 per annum. Salaries are determined based on scope and responsibilities and other factors.
Seniority level
Not Applicable
Employment type
Full‑time
Job function
Research, Analyst, and Information Technology
Research Services and Higher Education
Referrals increase your chances of interviewing at SLAC National Accelerator Laboratory by 2x.
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Position Overview
The Chemistry and Catalysis Division at the Stanford Synchrotron Radiation Lightsource (SSRL), SLAC National Accelerator Laboratory invites applications for a Research Associate position focused on computational and operando spectroscopy theory for catalytic and electrochemical systems. This role combines advanced electronic‑structure simulations, workflow development, and data‑centric analysis to interpret operando X‑ray absorption spectroscopy (XAS) data and bridge theory with experiment.
The successful candidate will develop and apply first‑principles methods to model and interpret X‑ray absorption spectra of catalytic and electrochemical systems, including batteries, under realistic working conditions. A key aspect of this role will be the creation of integrated computational‑experimental workflows that convert raw experimental data into AI‑ready physical descriptors and interpretable insights that can ultimately support foundational and generative modeling approaches.
This position offers a unique opportunity to contribute to the multi‑laboratory ISAAC initiative, one of whose goals is to transform X‑ray absorption spectroscopy into a data‑driven science where simulations, experiments, and physics‑informed AI collectively advance our understanding of dynamic materials and interfaces.
Note: The Research Associate role is a fixed‑term staff position. This 2‑year fixed‑term position may be extended depending on project needs and funding.
Applicants must provide evidence of a completed PhD degree or confirmation of completion prior to starting. Include a cover letter, a statement of research area with brief summary of accomplishments, a curriculum vitae, a list of publications, and names of at least three references. SLAC is a DOE laboratory operated by Stanford University in Menlo Park, CA.
Your Specific Responsibilities Include
Perform first‑principles simulations (DFT, TDDFT, BSE) of X‑ray absorption spectra for catalysts and electrochemical interfaces under realistic environments.
Optimize extended systems and surfaces, model adsorbates and reaction intermediates, and compute thermodynamic descriptors relevant to catalysis and battery materials.
Develop and automate computational workflows on high‑performance computing platforms (NERSC, S3DF, Sherlock, etc.) for large‑scale spectral and structural modeling.
Integrate theoretical modeling with operando experimental data to extract interpretable, uncertainty‑calibrated descriptors suitable for AI‑ready databases.
Investigate the use of machine‑learning tools for physics‑informed insight extraction from simulated and experimental datasets.
Collaborate closely with experimental scientists to interpret high‑throughput operando X‑ray data and guide the design of new measurements.
Publish research findings in high‑impact journals and present results at major scientific meetings.
To Be Successful In This Position You Will Bring
Ph.D. in Physics, Chemistry, Materials Science, Chemical Engineering, or a related field.
Expertise in first‑principles calculations (DFT, TDDFT) and electronic‑structure theory.
Demonstrated experience with operando X‑ray absorption spectroscopy interpretation.
Demonstrated ability to model extended systems, surfaces, and adsorbates under electrochemical or catalytic conditions.
Experience optimizing computational workflows and using HPC facilities for large‑scale simulations.
Strong programming skills (Python, C++, or MATLAB) for data handling and workflow automation.
Proven publication record in computational spectroscopy, catalysis, or materials modeling.
In Addition, Preferred Requirements Include
Familiarity with high‑throughput or workflow‑based simulation environments and data formats compatible with AI/ML pipelines.
Experience integrating theoretical and experimental datasets into structured, reusable, and AI‑ready repositories.
Understanding of uncertainty quantification and physics‑informed modeling for spectroscopy.
Motivation to explore hybrid approaches that combine physical theory, machine learning, and generative AI.
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.
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 this job.
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.
Subject to and expected to comply with all applicable University policies and procedures.
Title Classification: Research Associate.
Job Code: 0127, Grade G.
Duration: 2‑year fixed term.
The expected pay range for this position is $70,000 – $100,000 per annum. Salaries are determined based on scope and responsibilities and other factors.
Seniority level
Not Applicable
Employment type
Full‑time
Job function
Research, Analyst, and Information Technology
Research Services and Higher Education
Referrals increase your chances of interviewing at SLAC National Accelerator Laboratory by 2x.
#J-18808-Ljbffr