Co-Op, Autonomous SEM
Core
Building autonomous workflows for Scanning Electron Microscopes (SEM) to enable high-throughput, consistent materials characterization and ML training data generation.
Role type
Co-op, Autonomous SEM Engineer (Materials Science/AI)
Builds
Autonomous SEM navigation, image acquisition logic, and closed-loop experimental workflows for materials discovery.
Domain
Materials Science / Autonomous Laboratory / AI for Science
Deliverable
production ML models
Required skills
Python for automation, electron optics expertise, image quality evaluation, closed-loop learning, active learning, Bayesian optimization, workflow documentation
Preferred skills
autonomous microscopy, self-driving labs, agentic scientific workflows, scientific image data analysis, image segmentation, particle finding, morphology analysis
Technologies
Python, vendor APIs, MCP servers, LLM-enabled workflows
Responsibilities
Support development of autonomous SEM workflow using vendor APIs; Test navigation logic for locating particles, surfaces, and regions of interest; Evaluate image quality using criteria such as focus, contrast, feature visibility, and sampling value; Support experiments that connect imaging decisions to downstream analysis and ML training needs; Document acquisition behavior, edge cases, and failure modes across sample types; Help define practical guardrails for autonomous SEM operation, including when to capture, reposition, zoom, or adjust parameters.
Seniority
Co-op (PhD student or recent PhD)
