Graduate Intern – AI-Assisted Autonomous Electron Microscopy

About the role

The DTSW at the National Laboratory of the Rockies (NLR) has an opening for a graduate intern to contribute to a cutting-edge project at the intersection of autonomous instrumentation, computer vision, and large language models (LLMs) for materials characterization.

Responsibilities

• Develop and validate automated Python scripting routines for electron microscope control, including image acquisition, stage manipulation, and adaptive data collection workflows.
• Build and test computer vision pipelines (e.g., segmentation, defect detection) for real-time analysis of scanning transmission electron microscopy (STEM) and scanning electron microscopy (SEM) images.
• Integrate large language model (LLM) interfaces for natural language command processing, automated report generation, and AI-guided experimental planning.
• Apply machine learning methods to grain analysis, particle characterization, and compositional mapping using STEM, SEM, and associated spectroscopic datasets.
• Collaborate with research staff to evaluate and iterate on autonomous workflows for throughput, reproducibility, and scientific fidelity.
• Document code, prepare technical summaries, and contribute to reports and publications as appropriate.

Requirements

• Minimum of a 3.0 cumulative grade point average.
• Undergraduate: Must be enrolled as a full-time student in a bachelor’s degree program from an accredited institution.
• Post Undergraduate: Earned a bachelor’s degree within the past 12 months.
• Graduate: Must be enrolled as a full-time student in a master’s degree program from an accredited institution.
• Post Graduate: Earned a master’s degree within the past 12 months.
• Graduate + PhD: Completed master’s degree and enrolled as PhD student from an accredited institution.

Qualifications

• Proficiency in Python programming, including experience with scientific libraries (NumPy, SciPy, Pandas, scikit-image, OpenCV, or equivalent).
• Experience applying machine learning or computer vision methods to image-based data (segmentation, classification, detection, or related tasks).
• Strong analytical and problem-solving skills, with attention to precision in experimental or computational workflows.
• Excellent written and verbal communication skills; ability to document and present technical work clearly.

Preferred Qualifications

• Prior hands-on experience analyzing microscopy images (SEM, TEM, optical, or equivalent), including grain analysis, particle segmentation, or defect characterization.
• Familiarity with large language model (LLM) APIs or frameworks (e.g., LangChain, OpenAI API, Hugging Face Transformers).
• Experience working with industrial or laboratory datasets in a research or applied context.
• Background in computational mathematics, data science, or a related quantitative field.
• Coursework or experience in materials characterization, electron microscopy, or related experimental methods is a plus but not required.