PhD position on Physics-Guided Digital Twin Development for Composition-Process-Structure-Property M
Core
Develop a physics-guided digital twin framework for magnesium alloys integrating crystal plasticity, phase-field, and machine learning to predict microstructure evolution and mechanical properties.
Role type
PhD researcher in computational materials engineering and machine learning
Builds
A unified digital-twin framework for composition-process-structure-property modeling
Domain
Materials science, computational mechanics, machine learning
Deliverable
production ML models
Required skills
Python programming, machine learning, crystal plasticity, phase-field modeling, experimental data analysis, uncertainty quantification, explainable AI (XAI)
Preferred skills
Finite element methods, thermodynamic simulations, analysis of microstructure/texture data
Technologies
Python, PyTorch, Scikit-Learn, SEM, EBSD, XRD
Responsibilities
Develop physics-guided ML models, implement simulation workflows, incorporate experimental data, validate predictions, publish scientific results
Seniority
PhD candidate (researcher)