Research Associate - Thin Films (Fixed Term)

About the role

We are seeking a hands-on Research Associate in Thin Films to execute advanced deposition and nanofabrication processes, generate high-quality experimental data, and collaborate closely with our AI and materials teams.

This is a 12-month fixed-term position with the potential to convert to full time.

Responsibilities

• Execute thin-film deposition and related nanofabrication processes — including PVD (sputtering, evaporation), PLD, and where relevant ALD — initially at partner cleanroom facilities such as Stanford Nanofab, transitioning to Periodic’s in-house lab as tools are commissioned.
• Prepare substrates, manage process flows, and maintain detailed experimental records that meet the metadata and data quality standards required for AI training.
• Perform structural and functional thin-film characterization: XRD/XRR for structure and thickness, ellipsometry and profilometry for film properties, SEM/EDX for morphology and composition, and 4-point probe and basic transport measurements for electrical properties.
• Support in-situ metrology during deposition: monitor RHEED during PLD for epitaxial growth quality and ellipsometry during PVD for real-time thickness control.
• Collaborate with the AI and materials science teams to close the bulk-to-thin-film property gap — helping define which deposition parameters to vary, interpreting film characterization results in context of what the AI predicts, and flagging discrepancies that may indicate new physics or synthesis insights.
• Troubleshoot process issues and iterate quickly on recipes under guidance from senior team members. Escalate anomalies rather than working around them, and document both failures and fixes in a format that preserves institutional knowledge.
• Follow rigorous laboratory safety and facility protocols, including at external partner facilities with their own cleanroom safety requirements.

Requirements

• Currently pursuing or recently completed a PhD (or advanced graduate degree) in materials science, physics, chemistry, or a related field — or equivalent hands-on experience in research labs or process engineering.
• Strong background in nanofabrication or thin-film processing, developed in a university or research lab environment. You have spent real time at a tool, not just observed someone else operate it.
• Hands-on experience operating thin-film deposition equipment: sputtering, evaporation, PLD, ALD, or related techniques. Familiarity with the practical realities of these systems — target conditioning, chamber qualification, substrate preparation, and recipe troubleshooting.
• Basic thin-film characterization experience: you know how to read an XRD pattern, interpret an ellipsometry fit, and recognize a SEM image that signals a process problem.
• Strong documentation habits and attention to detail. You log what you did, not just what you intended to do, and you understand why that distinction matters in a data-driven science environment.
• Ability to ramp up quickly on new equipment and experimental workflows, and comfort operating independently in shared research facilities where you are responsible for your own training and access.
• Experience working in university nanofabrication facilities or shared cleanroom environments — including completing facility-specific safety training, navigating tool reservation systems, and operating within shared-use norms.
• Exposure to functional materials in thin-film form: superconductors, magnetics, ferroelectrics, thermoelectrics, or multi-layer device stacks relevant to memory or semiconductor applications.
• Familiarity with LIMS or other lab information systems used to track samples, experiments, and characterization results — and the instinct to treat data logging as part of the experiment, not an afterthought.
• Experience with wafer-level metrology: film thickness mapping, stress/warpage measurement, or 4-point probe resistivity mapping at wafer scale rather than just coupon scale.
• Interest in working at the intersection of experimental science and AI-driven discovery — curiosity about what our models predict and what that means for how you design the next experiment.