Research Fellow (Physics-Informed AI and Computational Materials Mechanics)
Core
Develop physics-informed AI models to discover damage-tolerant structural alloys for extreme environments by integrating AI, computational mechanics, and experimental data.
Role type
Research Fellow (Physics-Informed AI and Computational Materials Mechanics)
Builds
Surrogate models for predicting deformation and fracture in structural alloys
Domain
Materials Science / Computational Mechanics / AI for Science
Deliverable
production ML models
Required skills
Physics-informed AI, Crystal plasticity finite element modelling, Phase-field simulations, Python, Scientific computing, Machine learning frameworks (PyTorch/TensorFlow), Microstructure-property relationships, Fracture mechanics
Preferred skills
Simulation-experiment data integration, Inverse design, Uncertainty quantification
Responsibilities
Develop physics-informed AI and surrogate models for predicting deformation and fracture; 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
