Undergraduate Research Associate

About the role

The Graduate Research Assistant Program in computational materials science and data-driven experimental design is available in the Materials Science and Technology Division at Los Alamos National Laboratory (LANL).

Responsibilities

• Develop and implement advanced computational algorithms for identifying spatial and compositional patterns in multi-element mapping datasets.
• Design and recommend follow-on measurement actions to maximize information gain, including identifying regions for additional raster scans, suggesting higher-resolution or higher-statistics scans, and proposing targeted point analyses.
• Optimize acquisition parameters to enhance data quality while respecting operational and instrument constraints.
• Detect and manage out-of-bounds or invalid parameter selections by incorporating safe operating limits into the decision-making framework.
• Integrate uncertainty quantification and data-quality metrics to prioritize measurements and avoid low-value or unreliable regions.
• Implement the developed methods in Python-based, open-source software with a focus on reproducibility, modular architecture, and transparency.

Requirements

• Strong programming experience in Python, including scientific computing libraries (NumPy, SciPy, scikit-learn, PyTorch/TensorFlow, etc.)
• Experience with machine learning, AI methods, or data-driven modeling
• Familiarity with uncertainty quantification, statistical inference, or Bayesian experimental design
• Experience analyzing spatially resolved or imaging datasets
• Demonstrated ability to design and implement modular, reproducible scientific software
• Experience working with scientific instrumentation data or experimental workflows (preferred)
• Demonstrated ability to conduct independent and collaborative research
• Strong written and oral communication skills

Qualifications

• Enrolled in a Ph.D. program in materials science, computer engineering, or a closely related discipline

Skills

• Python programming
• Machine learning and AI methods
• Data-driven modeling
• Spatial analysis and imaging datasets
• Uncertainty quantification and statistical inference
• Scientific software development
• Scientific instrumentation data analysis
• Independent and collaborative research skills
• Effective communication skills

Benefits

Not specified

Pay

Not specified

Schedule

Not specified