Postdoc: Autonomous Semiconductor Experimentation

National Renewable Energy Laboratory; Golden (CO); On-site; USD 77,000 - 126,000; Full time

Benefits offered by this job

Medical, dental, and vision insurance

Pension benefits

403(b) Employee Savings Plan

Job summary

National Renewable Energy Laboratory in Golden, CO, invites applications for a Postdoctoral Researcher focused on autonomous experimentation for semiconductor materials. The role sits at the intersection of AI, automation, and advanced thin-film instrumentation, and aims to accelerate discovery of oxide, nitride, and related materials.

The candidate will design closed-loop workflows, integrate instruments and data pipelines, and collaborate with scientists to deploy AI-driven methods across

Qualifications

• Must be a recent PhD graduate within the last three years.
• Hands-on proficiency in Python and scientific software development.
• Experience with machine learning, statistical modeling, and data-driven methods.
• Familiarity with vacuum-based thin-film processing and characterization is preferred.

Responsibilities

• Design and deploy closed-loop workflows for autonomous experimentation on synthesis and characterization instruments.
• Integrate laboratory instrumentation, data pipelines, and workflow orchestration into scalable platforms.
• Apply AI methods to materials discovery and optimize experimental design under safety procedures.
• Collaborate with researchers to apply autonomous approaches across projects.

Skills

Python

AI/ML concepts

Active learning

Bayesian optimization

Design of experiments

Uncertainty quantification

Education

PhD in Materials Science, Chemistry, Physics, or related field

Tools

SCPI

Modbus RTU/TCP

OPC UA

NATS/MQTT/ZeroMQ

Job description

National Renewable Energy Laboratory in Golden, CO, invites applications for a Postdoctoral Researcher focused on autonomous experimentation for semiconductor materials. The role sits at the intersection of AI, automation, and advanced thin-film instrumentation, and aims to accelerate discovery of oxide, nitride, and related materials.

The candidate will design closed-loop workflows, integrate instruments and data pipelines, and collaborate with scientists to deploy AI-driven methods across