AI-Driven Materials Discovery Fellow

NANYANG TECHNOLOGICAL UNIVERSITY; Singapore; On-site; SGD 60,000 - 90,000; Full time

Job summary

NTU Singapore’s School of Materials Science and Engineering seeks a Research Fellow to advance AI-driven and data-driven discovery of functional materials. You will develop ML models, perform high-throughput DFT/MD simulations, and build datasets to accelerate energy storage, energy conversion, and electronic materials research.

Collaborating with experimental groups, you will interpret results, guide materials selection, and contribute to manuscript preparation and funding reports.

Qualifications

• PhD in a related discipline.
• Demonstrated research experience in computational materials science, materials informatics, machine learning for materials, or AI-driven materials discovery.
• Strong background in first-principles calculations (DFT), molecular dynamics, and/or high-throughput computational screening.
• Experience in ML/DL areas such as graph neural networks, generative models, transfer or active learning.
• Proficiency in scientific programming and materials simulation software.
• Strong publication record in reputable journals and ability to drive research tasks to completion.
• Excellent written and oral communication for collaboration across disciplines.

Responsibilities

• Develop and apply machine learning and deep learning models for materials property prediction, interpretation, and inverse design.
• Perform high-throughput DFT and MD simulations to understand mechanisms and screen candidate materials for energy and electronic applications.
• Build and curate materials datasets, workflows, and platforms for automated, high-throughput discovery.
• Collaborate with experimental groups to provide mechanistic insights and accelerate exploration.
• Investigate structure–property relationships in electrolytes, cathodes, semiconductors, and related materials.
• Develop interpretable, physics-informed AI approaches to extract insights from data.
• Assist in manuscript preparation, conference presentations, grant development, and reporting to funders.
• Mentor junior researchers and support lab management as needed.

Skills

Machine learning

Graph neural networks

Generative models

Transfer learning

Active learning

Materials informatics

Molecular dynamics

DFT

Programming

Education

PhD in Materials Science

Tools

DFT software

Molecular dynamics

High-throughput screening

Job description

NTU Singapore’s School of Materials Science and Engineering seeks a Research Fellow to advance AI-driven and data-driven discovery of functional materials. You will develop ML models, perform high-throughput DFT/MD simulations, and build datasets to accelerate energy storage, energy conversion, and electronic materials research.

Collaborating with experimental groups, you will interpret results, guide materials selection, and contribute to manuscript preparation and funding reports.