Research Fellow (Physics-Informed AI and Computational Materials Mechanics)
Core
Develop physics-informed AI models to predict deformation and fracture in structural alloys for extreme environments.
Role type
Research Fellow (Physics-Informed AI and Computational Materials Mechanics)
Builds
Surrogate models for deformation and fracture prediction; guides microstructure and additive manufacturing process optimisation.
Domain
Materials Science / Computational Mechanics / AI for Science
Deliverable
production ML models
Required skills
crystal plasticity finite element modelling, phase-field simulations, physics-informed machine learning, Python, scientific computing, microstructure–property relationships, fracture mechanics
Preferred skills
simulation–experiment data integration, inverse design, uncertainty quantification
Technologies
PyTorch, TensorFlow
Responsibilities
Develop physics-informed AI and surrogate models; Integrate AI with crystal plasticity finite element and phase-field simulations; Combine simulation and experimental data for model training, calibration and validation; Apply inverse design and optimisation to identify damage-tolerant microstructures; Collaborate with experimental researchers to interpret mechanisms and validate model predictions; Assess model accuracy, uncertainty and applicability; Publish research findings, contribute to project reports and mentor students.
