The School of Materials Science and Engineering (MSE) provides a vibrant and nurturing environment for staff and students to carry out inter-disciplinary research in key areas such as Computational Materials Science, Characterisation Materials Science, Defence Composite Materials, Functional Composite Materials, Energy, Nanomaterials, Low Dimensional Materials, Biomaterials Materials, Biological Materials, Bioinspired Materials and Sustainable Materials.

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

We are looking for a Research Fellow to contribute to the development of a Materials Generative Design and Validation Framework. The role will work at the intersection of machine learning, high-throughput experimentation, and materials discovery, focusing on accelerating the design and synthesis of novel materials.

Key Responsibilities:

• Develop and apply generative AI models for materials discovery, leveraging deep learning, Bayesian optimization, and active learning.
• Integrate computational materials science techniques (DFT, MD, machine learning force field modelling) with data-driven approaches.
• Work with team to design and implement high-throughput experimental workflows for rapid materials synthesis and characterization.
• Collaborate with interdisciplinary teams, including chemists, physicists, and AI/ML experts, to refine generative models with experimental feedback.

Job Requirements:

• PhD in Materials Science, Chemistry, Physics, Computer Science, or a related field.
• Strong expertise in machine learning for materials science (e.g., generative models, neural networks, active learning).
• Hands-on experience with computational materials methods (e.g., DFT, molecular dynamics, phase-field simulations).
• Proficiency in Python, TensorFlow/PyTorch, and scientific computing.
• Experience in handling large experimental and computational datasets.

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

Hiring Institution: NTU