Co-Op, Autonomous SEM
On-siteCambridge, Massachusetts, United States
Job Summary
Develop autonomous SEM workflows using vendor APIs to support consistent, high-throughput characterization. Test navigation logic for locating particles and regions of interest while evaluating image quality based on focus, contrast, and feature visibility. Document acquisition behavior, edge cases, and failure modes across diverse sample types. Collaborate with ML and experimental scientists to connect imaging decisions to downstream analysis and training needs. Define practical guardrails for autonomous operation, including capture, repositioning, and parameter adjustments. Requires a PhD in Materials Science or related field with Python experience in automated scientific workflows.
Required Qualifications
- Currently pursuing a PhD or have completed a PhD in Materials Science, Chemistry, Chemical Engineering, Physics, Applied Physics, Computer Science, or a related technical field
- Experience building automated scientific or laboratory workflows using Python
- Deep understanding of electron optics, electron-beam interaction with matter, column alignments, stigmation correction, and source dynamics
- Familiarity with MCP servers, LLM-enabled workflows, or agentic control of scientific instruments
- Experience with closed-loop learning, active learning, Bayesian optimization, or reward-driven experimental workflows
- Ability to translate expert instrument operations into clear, testable, and well-documented workflow logic
Desired Qualifications
- Experience with autonomous microscopy, self-driving labs, or agentic scientific workflows
- Experience with data analysis for scientific images, spectra, or microscopy datasets
- Experience with image segmentation, particle finding, feature detection, or morphology analysis
- Interest in building practical autonomy for scientific instruments across real sample types and workflows
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