Data-Driven Atomic Catalysts Research Fellow (DFT/ML)

Nanyang Technological University; Singapore; On-site; SGD 60,000 - 90,000; Full time

Job summary

Nanyang Technological University (NTU) Singapore invites applications for a Research Fellow to contribute to data-driven discovery of atomic catalysts. The role focuses on theoretical predictions with DFT and machine learning, coupled with mechanistic insights in renewable energy materials.

The candidate will collaborate across disciplines and support lab operations and grant writing. Strong experience with DFT/ML and a solid publication record are valued, and entry-level candidates are welcome

Qualifications

• PhD in material sciences or applied physics required.
• Experience applying DFT and machine learning to atomic catalysts preferred.
• Strong publication record in renewable energy materials, electrocatalysis, or related fields.
• Entry-level candidates are welcome to apply.
• Demonstrated motivation for multidisciplinary, high-impact research.
• Excellent teamwork and ability to support a dynamic research group.

Responsibilities

• Perform theoretical predictions using DFT and machine learning to understand atomic catalysts.
• Investigate mechanisms using in-situ and operando techniques and theory.
• Coordinate collaboration with researchers to maintain steady progress and support experiments.
• Support project management and lab infrastructure, including procurement and setting up equipment.
• Prepare regular progress reports to PI and contribute to grant proposals.

Skills

DFT

Machine learning

Publication record

Education

PhD in material sciences or applied physics

Job description

Nanyang Technological University (NTU) Singapore invites applications for a Research Fellow to contribute to data-driven discovery of atomic catalysts. The role focuses on theoretical predictions with DFT and machine learning, coupled with mechanistic insights in renewable energy materials.

The candidate will collaborate across disciplines and support lab operations and grant writing. Strong experience with DFT/ML and a solid publication record are valued, and entry-level candidates are welcome