Research Fellow - Physics-Informed AI and Computational Materials Mechanics

About the role

The School of Mechanical & Aerospace Engineering (MAE) at NTU is looking for a Research Fellow to develop physics-informed AI models for discovering damage-tolerant structural alloys for extreme environments. The role integrates AI, computational mechanics and experimental data to predict deformation and fracture behaviour and to guide microstructure and additive manufacturing process optimisation.

Key responsibilities

• Develop physics-informed AI and surrogate models for predicting deformation and fracture in structural alloys.
• 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 and guide manufacturing processes.
• Collaborate with experimental researchers to interpret mechanisms and validate model predictions.
• Assess model accuracy, uncertainty and applicability across microstructures and environmental conditions.
• Publish research findings, contribute to project reports and mentor students.

Required profile

• PhD in Materials Science and Engineering, Mechanical Engineering, Computational Mechanics or a closely related field.
• Strong knowledge of computational mechanics or computational materials science, with hands-on experience in crystal plasticity finite element modelling and/or phase-field simulations.
• Experience developing AI or machine learning models for physics-based scientific or engineering applications.
• Good understanding of microstructure-property relationships and deformation mechanisms in structural materials; knowledge of fracture mechanics is highly desirable.
• Experience with simulation-experiment data integration, physics-informed learning, inverse design or uncertainty quantification is advantageous.
• Strong analytical and communication skills, able to work independently and collaborate across disciplines.

Required skills

• Proficiency in Python, scientific computing and machine learning frameworks such as PyTorch or TensorFlow.
• Crystal plasticity finite element modelling and/or phase-field simulations.

Only shortlisted candidates will be notified.