Research Fellow (Physics-Informed AI and Computational Materials Mechanics)

The School of Mechanical & Aerospace Engineering (MAE) is a robust, dynamic and multi-disciplinary international research community comprising of world-class scientists and bright students. MAE prides itself in its excellent research capabilities in areas including advanced manufacturing, aerospace, biomedical, energy, industrial engineering, maritime engineering, robotics, etc. The school is equipped with state-of-the-art research infrastructure, housing a comprehensive range of cluster laboratories, test bedding facilities, research centres/institutes and corporate laboratories. Cutting-edge research in MAE addresses the immediate needs of our industries and supports the nation’s long-term development strategies. In the new era of industrial 4.0 and sustainable living, MAE is rigorous in developing new competencies to support the growth and competitiveness of our engineering sector in the global landscape. MAE has grown to be leader in Engineering Research, ranking amongst the top engineering schools in the world.

For more details, please view https://www.ntu.edu.sg/mae/research.

We are looking for a Research Fellow to develop physics-informed AI models for the discovery of damage-tolerant structural alloys for extreme environments. The role will integrate AI, computational mechanics and experimental data to predict deformation and fracture behaviour and guide microstructure and additive manufacturing process optimisation.

• →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.

• 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 in developing AI or machine learning models for physics-based scientific or engineering applications.
• Proficiency in Python, scientific computing and machine learning frameworks such as PyTorch or TensorFlow.
• 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 would be advantageous.
• Strong analytical and communication skills, with the ability to work independently and collaborate across disciplines.

We regret to inform that only shortlisted candidates will be notified.