AI-Driven Materials Science Research Fellow

National University of Singapore; Singapore; On-site; SGD 60,000 - 90,000; Full time

Job summary

National University of Singapore invites applications for a Research Fellow (AI for Materials Science) in the Department of Materials Science and Engineering at Kent Ridge Campus. The position focuses on atomistic and generative modelling, agentic AI for physical science, and the alignment of theory with experiment.

The successful candidate will design and execute innovative research, collaborate with experimentalists, and contribute to advancing AI methods for materials discovery and

Qualifications

• PhD in Science or Engineering required.
• Background in applied mathematics with interest in physical sciences.
• Strong foundation in thermodynamics & statistical mechanics, solid-state physics/chemistry, or quantum mechanics.
• Hands-on experience with density functional theory (DFT) calculations (VASP, Quantum Espresso, Q-Chem, PySCF).
• Proficiency in at least one deep learning framework (PyTorch or JAX).
• Expertise in atomistic thermodynamic/kinetic simulations or computational chemistry.
• Ability to independently design and execute research projects; excellent English written and spoken.

Skills

DL frameworks

Independent research

English proficiency

Education

PhD in science/engineering

Applied mathematics background

Tools

DFT software

Job description

Job Title: Research Fellow (AI for Materials Science)

University-Level Unit: College of Design and Engineering

Faculty/Department-Level Unit: Materials Science and Engineering

Employee Category: Research Staff

Location_ONB: Kent Ridge Campus

Posting Start Date: 11/08/2026

Job Description

A Research Fellow (AI for Materials Science) position is available in the Department of Materials Science and Engineering at the National University of Singapore under the direction of Dr. Peichen Zhong. We are seeking a candidate who is interested in pursuing research in atomistic and generative modelling, agentic AI for physical science, theory & experimental alignment, and other related fields.

Qualifications

• PhD degree in Science or Engineering
• Education or a track record in applied mathematics, with a strong interest in physical sciences
• A solid foundation in thermodynamics & statistical mechanics, solid-state physics/chemistry, and/or quantum mechanics
• Practical experience with density functional theory calculations (e.g., VASP, Quantum Espresso, Q-Chem, PySCF)
• Proficiency in at least one mainstream deep learning framework (e.g., PyTorch, JAX)
• Expertise in (atomistic) thermodynamic, kinetic simulations or computational chemistry
• Ability to independently design and execute research projects and excellent proficiency in written and oral English.
  
  

Req ID: 34034