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

Nanyang Technological University; Singapore; On-site; SGD 90,000 - 120,000; Full time

Job summary

Nanyang Technological University (NTU) Singapore invites applications for a Research Fellow to develop physics-informed AI models for damage-tolerant structural alloys under extreme environments. The role merges AI, computational mechanics and experimental data to guide microstructure and additive manufacturing process optimisation.

Applicants should have a PhD in relevant field, strong hands-on experience in crystal plasticity finite element modelling or phase-field simulations, and proficiency

Qualifications

• PhD in Materials Science and Engineering, Mechanical Engineering, Computational Mechanics, or a closely related field.
• Strong knowledge of computational mechanics with hands-on experience in crystal plasticity FE modelling and/or phase-field simulations.
• Experience in developing AI or ML models for physics-based scientific or engineering applications.
• Proficiency in Python and ML frameworks such as PyTorch or TensorFlow.
• Good understanding of microstructure–property relationships and deformation mechanisms.
• Experience with simulation–experiment data integration, physics-informed learning or uncertainty quantification would be advantageous.
• Strong analytical and communication skills; ability to work independently and across disciplines.

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.

Skills

Computational mechanics

Machine learning for physics

Scientific programming

Crystal plasticity

Education

PhD in Materials Science and Engineering

Tools

PyTorch

TensorFlow

Python

Phase-field simulations

Crystal plasticity FE

Job description

About the School

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.

Position Overview

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.

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.

Job Requirements

• 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.

Hiring Institution

We regret to inform that only shortlisted candidates will be notified. Hiring Institution: NTU A research-intensive public university, Nanyang Technological University, Singapore (NTU Singapore) has 33,000 undergraduate and postgraduate students in the Engineering, Business, Science, Humanities, Arts, & Social Sciences, and Graduate colleges. It also has a medical school, the Lee Kong Chian School of Medicine, established jointly with Imperial College London. NTU is also home to world-class autonomous institutes – the National Institute of Education, S Rajaratnam School of International Studies, Earth Observatory of Singapore, and Singapore Centre for Environmental Life Sciences Engineering – and various leading research centres such as the Nanyang Environment & Water Research Institute (NEWRI) and Energy Research Institute @ NTU (ERI@N). Ranked amongst the world’s top universities by QS, NTU has also been named the world’s top young university for the past seven years. The University’s main campus is frequently listed among the Top 15 most beautiful university campuses in the world and has 57 Green Mark-certified (equivalent to LEED-certified) buildings, of which 95% are certified Green Mark Platinum. Apart from its main campus, NTU also has a campus in Novena, Singapore’s healthcare district. Under the NTU Smart Campus vision, the University harnesses the power of digital technology and tech-enabled solutions to support better learning and living experiences, the discovery of new knowledge, and the sustainability of resources. For more information, visit www.ntu.edu.sg About NIE About RSIS About LKCMedicine About NTUItive