Graduate Intern - AI-Assisted Autonomous Electron Microscopy

Posting Title

Graduate Intern - AI-Assisted Autonomous Electron Microscopy

.

Location

CO - Golden

.

Position Type

Intern (Fixed Term)

.

Hours Per Week

40

.

Working at NLR

NLR is located at the foothills of the Rocky Mountains in Golden, Colorado is the nation's primary laboratory for energy systems research and development.

Join the National Laboratory of the Rockies (NLR), where world-class scientists, engineers, and experts are accelerating energy innovation through breakthrough research and systems integration. From our mission to our collaborative culture, NLR stands out in the research community for its commitment to an affordable and secure energy future. Spanning foundational science to applied systems engineering and analysis, we focus on solving complex challenges to deliver advanced, secure, reliable, and cost-effective energy solutions.

Our work helps strengthen U.S. industries, support job creation, and promote national economic growth.

At NLR, you'll find a mission-driven environment supported by state-of-the-art facilities, multidisciplinary research teams, and strong collaborations with industry, academia, and other national laboratories. We offer robust professional development opportunities, and a competitive benefits package designed to support your career and well-being.

Job Description

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.

Responsibilities include:

* 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.

.

Basic Qualifications

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. Eligible for an internship period of up to one year.

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. Eligible for an internship period of up to one year.

Graduate + PhD:
Completed master's degree and enrolled as PhD student from an accredited institution.

Please Note:

* Applicants are responsible for uploading official or unofficial…