Scientist TEM Analysis and Automation

Job Description

Phoenix, AZ | Weekend Shift Eligible

Eurofins EAG Laboratories is seeking a hands‑on Transmission Electron Microscopy (TEM) specialist with strong programming and automation experience to enhance the collection, processing, and analysis of microscopy data.

This position is not a general data science role. The primary focus is on developing and implementing automation directly on S/TEM systems to improve throughput, consistency, and data quality within a high‑volume, client‑facing laboratory environment.

The Scientist will collaborate closely with engineers, scientists, and operations teams to translate laboratory requirements into practical, scalable automation solutions and data workflows.

This role is centered on materials science applications within an analytical laboratory setting. The position requires experience aligned with materials characterization and non‑biological systems, which are most relevant to the work performed. Responsibilities include a combination of hands‑on laboratory execution and applied programming, rather than work that is solely research‑or‑theory based.

Work is conducted in a client‑driven analytical services environment, requiring solutions that are practical, scalable, and reproducible.

• Design, build, and implement automation workflows directly on S/TEM systems (data acquisition, instrument control, and workflow scripting)
• Develop and maintain automated pipelines for TEM data collection and analysis
• Perform image processing (segmentation, enhancement, measurement, visualization) on 2D and 3D datasets
• Apply computer vision, statistics, and machine learning to microscopy and analytical data
• Work with multi‑modal materials characterization data (TEM, SEM, EDS, XRD, XPS, RBS, etc.)
• Improve throughput, repeatability, and data quality of TEM‑based services
• Build tools for data processing, feature extraction, and exploratory analysis
• Partner with lab teams to translate real‑world analytical problems into automated solutions
• Support S/TEM operation and data collection, including EDS/EELS where needed
• Contribute to new TEM capabilities (e.g., 4D STEM, advanced imaging workflows)

• Implement or improve automation workflows used in production TEM work
• Reduce manual intervention in data collection and analysis
• Increase throughput and consistency of TEM services
• Build usable tools and pipelines that lab teams adopt

• Wednesday–Sunday or Saturday–Wednesday
• Weekend shift differential applies

This position supports work related to defense applications and is subject to ITAR regulations. Applicants must be U.S. Persons, defined as: U.S. Citizen, U.S. Permanent Resident (Green Card holder), Political Asylee or Refugee.

• MS or PhD in Materials Science, Physics, Engineering, or related field
• Hands‑on S/TEM experience, including real‑world data collection
• Experience developing or implementing automation for TEM or similar instrumentation (e.g., scripting acquisition, using vendor APIs, workflow automation)
• Strong Python programming skills (Ability to build analysis tools, not just basic scripts)
• Experience applying data science, statistics, or computer vision to experimental data
• Experience working with materials‑focused systems (e.g., semiconductors, metals, ceramics, failure analysis)
• Experience with EDS (not just exposure to EELS)
• Ability to work in a fast‑paced, production lab environment

• Experience with machine learning applied to imaging or scientific datasets
• Familiarity with deep learning frameworks (PyTorch, Tensor Flow)
• Experience with TEM automation frameworks or vendor APIs
• Experience with large‑scale or high‑throughput data pipelines
• Exposure to 4D STEM or advanced TEM techniques
• Experience working with multi‑modal data (image, spectral, time‑series)

• Ability to continuously operate a personal computer or lab equipment for extended periods of time (4 or more hours).
• Ability to operate a keyboard and other lab equipment, use a telephone, access file cabinets and other items stored at various levels, including…