Research Associate-Experimental

About the role

The Stanford Synchrotron Radiation Lightsource at SLAC National Accelerator Laboratory seeks a postdoctoral Research Associate to apply in‑situ and operando synchrotron X‑ray characterization to develop earth‑abundant cathodes for Li‑metal and Na‑ion batteries. The work will focus on high‑capacity, long‑lasting cathodes and anodes, using techniques such as transmission X‑ray microscopy, X‑ray diffraction, and X‑ray spectroscopy to study electrochemical cycling, degradation mechanisms, and novel nondestructive detection of Na metal plating. The position offers opportunities to publish lead‑author manuscripts, mentor undergraduate researchers, and build a career in energy‑storage technologies or X‑ray science.

This is a fixed‑term staff position with an initial 12‑month appointment, renewable based on project needs and funding.

Responsibilities

• Develop and characterize high‑capacity, earth‑abundant cathodes for Li‑metal and Na‑ion batteries.
• Investigate low‑Ni, high‑Fe layered‑oxide Na‑ion cathodes, Na‑ion anodes beyond hard carbon, and low/no‑Co high‑capacity Li‑metal cathodes.
• Apply transmission X‑ray microscopy, X‑ray diffraction, and X‑ray spectroscopy during battery cycling to monitor particle fracturing, interparticle oxidation heterogeneity, irreversible chemical reactions, and crystal‑structure evolution.
• Develop novel nondestructive techniques for direct detection of Na metal plating on hard carbon.
• Analyze large X‑ray datasets, preferably using Python or similar tools.
• Publish lead‑author manuscripts and present findings at scientific meetings.
• Mentor undergraduate researchers, particularly during summer projects.

Qualifications

• Ph.D. in physics, materials science, chemistry, engineering, or a related field (or completion of Ph.D. requirements prior to start date).
• Demonstrated experience with synchrotron X‑ray characterization of battery materials.
• Strong background in electrochemistry.
• Excellent experimental, analytical, and computational skills.
• Experience analyzing large datasets using Python or comparable analysis tools (preferred).
• Broad scientific interests and willingness to learn new techniques.
• Effective written and verbal communication skills.
• Ability to work collaboratively with a diverse team and strong interpersonal skills.
• Capacity to work both independently and as part of a team.

Application Materials

Applicants should submit a cover letter, a curriculum vitae including a list of publications, and the names of three references for future letters of recommendation.