Scientist, TEM Analysis

Eurofins USA Material Sciences•Phoenix, AZ

1d•Onsite

About The Position

Eurofins EAG Laboratories has over 40 years of experience in engineering and materials testing services. As part of Eurofins Scientific, a global leader in scientific services, EAG focuses on unlocking the capabilities of materials and products through advanced analysis. The company supports industries from semiconductors to pharmaceuticals, using techniques in analytical chemistry, microscopy, surface analysis, and engineering sciences to solve complex challenges. This role is for a Scientist, TEM/Data Science, to support and advance data-driven analysis across materials characterization and analytical testing services, with a strong emphasis on Transmission Electron Microscopy (TEM). The position involves applying data science, statistics, and machine learning to complex, multi-modal laboratory data from various analytical techniques. The Scientist will collaborate with engineers, scientists, and operational teams to enhance data quality, automation, insight generation, and analytical capabilities for client-driven testing services.

Requirements

MS or PhD in Materials Science, Data Science, Engineering, Physics, Chemistry, or a related field.
Strong background in TEM principles, operation of S/TEM, EDS/EELS, and automation.
Demonstrated experience applying data science or statistical methods in a laboratory, engineering, or applied science environment.
Proficiency in Python and common scientific data analysis libraries (e.g., NumPy, Pandas, SciPy, scikit-learn).
Experience working with experimental, sensor, image, spectral, or time-series data.
Strong written and verbal communication skills.
Strong foundation in statistics, data analysis, and applied machine learning.
Ability to evaluate model performance and understand limitations of scientific datasets.
Familiarity with machine learning frameworks such as PyTorch or TensorFlow for applied use cases.
Experience with data visualization and scientific reporting.
Ability to work collaboratively in a fast-paced, client-focused analytical services environment.
All considered applicants must be U.S. Persons as defined by ITAR: U.S. Citizen, U.S. Permanent Resident (i.e. “Green Card Holder”), or Political Asylee or Refuge.

Nice To Haves

Advanced Machine Learning & Modeling Experience applying advanced machine learning techniques such as deep learning or representation learning to scientific datasets.
Familiarity with image, spectral, time-series, or multi-modal data modeling.
Exposure to modern neural network architectures (e.g., CNNs, RNNs, transformers) and understanding when advanced methods are appropriate.
Knowledge of signal processing or feature extraction techniques used in scientific instrumentation data.
Familiarity with model lifecycle practices such as experiment tracking, versioning, and reproducibility.

Responsibilities

Support the data process from various materials characterization techniques including TEM, SEM, EDS, XRD, XPS and RBS.
Develop and tune TEM automation on cutting edge TEM microscopes.
Perform image processing - including image enhancement, segmentation, measurement and visualization on 2D and 3D data sets.
Responsible for building algorithms and developing software to automate the TEM data collection and processing using computer science techniques (computer vision, machine learning, etc.).
Apply data science and machine learning methods to analyze, model, and interpret materials characterization data across multiple analytical techniques, including microscopy, spectroscopy, diffraction, and other physical or chemical testing methods.
Proactive in developing new TEM technologies (such as 4D STEM) to meet new demands from customers.
Collect S/TEM, EDS/EELS data and prepare reports per instructions.
Support TEM jobs including S/TEM, EDS/EELS.
Develop and maintain data analysis workflows and pipelines that improve efficiency, reproducibility, and scalability of laboratory data processing.
Perform statistical analysis, feature extraction, and exploratory data analysis to identify trends, anomalies, and correlations in experimental data.
Build, validate, and document predictive or classification models to support materials characterization, failure analysis, process optimization, and customer reporting.
Collaborate with laboratory scientists and engineers to translate experimental and customer questions into data-driven solutions.
Support automation and standardization of data collection, processing, and reporting where appropriate.
Communicate analytical results clearly to technical and non-technical stakeholders.
Contribute to continuous improvement initiatives related to analytical capabilities, data infrastructure, and quality systems.