Graduate Intern – AI-Assisted Autonomous Electron Microscopy
About The Position
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. This project offers a unique opportunity to advance the "self-driving" capabilities of electron microscopes by codifying expert experimental protocols into robust, executable algorithms. The intern will develop Python-based scripting routines to automate image acquisition, elemental analysis, and real-time experimental adjustments — enabling intelligent, adaptive operation across a range of materials relevant to energy, microelectronics, and power technologies. Working alongside experienced researchers in materials science and data science, the intern will integrate LLMs to enhance natural language processing of microscope commands, automate reporting workflows, and guide experimental decision-making. The project further explores how machine learning and computer vision can enable autonomous region-of-interest detection, defect identification, and compositional mapping at the nanoscale.
Requirements
- Minimum of a 3.0 cumulative grade point average.
- 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.
- Must meet educational requirements prior to employment start date.
- Must be enrolled as a full-time student in a master’s degree program from an accredited institution OR have earned a master’s degree within the past 12 months and be eligible for an internship period of up to one year OR be enrolled as a PhD student from an accredited institution after completing a master's degree.
- Must be enrolled as a full-time student in a bachelor’s degree program from an accredited institution OR have earned a bachelor’s degree within the past 12 months and be eligible for an internship period of up to one year.
Nice To Haves
- 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.
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.
Benefits
- medical, dental, and vision insurance
- 403(b) Employee Savings Plan with employer match
- sick leave (where required by law)
- performance-, merit-, and achievement- based awards that include a monetary component
- relocation expense reimbursement
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What This Job Offers
Job Type
Full-time
Career Level
Intern
