Master thesis: Automation of X-ray Diffraction (XRD) Pattern Analysis Using
Core
Develop machine learning methods for automated and accelerated analysis of X-ray diffraction (XRD) data to identify phase constituents, perform peak fitting, and extract microstructural information for cemented carbide grades and PVD/CVD coatings.
Role type
Master thesis researcher (Machine Learning & Materials Characterization)
Builds
A database of simulated diffraction patterns and ML models for high-throughput materials characterization
Domain
Materials Science / Machine Learning / X-ray Diffraction
Deliverable
production ML models
Required skills
Machine learning, programming, data-driven methods, X-ray diffraction analysis, phase identification, peak fitting, Rietveld refinement
Preferred skills
Materials science, applied physics, data science
Technologies
XRD, Rietveld refinement, ML frameworks
Responsibilities
Generate a database of simulated diffraction patterns, train models to automatically identify phase constituents, perform peak fitting, extract microstructural parameters (phase fractions, crystallographic texture, microstrain), evaluate ML approaches as complements or alternatives to traditional workflows
Seniority
Master's thesis student