PhD Studentship: Calibration Methodologies for Industrial/Geophysics Granular Materials
Core
Developing robust AI-enhanced calibration methodologies for particulate continuum models and particle simulations of industrial and geophysical granular materials.
Role type
PhD Researcher (Calibration Methodologies)
Builds
AI-informed calibration strategies for discrete particle models
Domain
Geophysics / Industrial Materials / Computational Mechanics
Deliverable
research
Required skills
Bayesian optimisation, reinforcement learning, deep learning, surrogate modelling, uncertainty quantification, contact model parameterisation, experimental data analysis
Preferred skills
machine learning for parameter optimisation, AI-driven model selection
Technologies
characterisation machines, discrete particle models
Responsibilities
Develop indirect or bulk calibration methods using characterisation machines; integrate machine learning for parameter optimisation and model selection; employ surrogate modelling to reduce computational costs; perform AI-based uncertainty quantification
Seniority
PhD Candidate