Senior Scientist, Sustainability: Computational Catalysis

A*STAR - Agency for Science, Technology and Research; Singapore; On-site; SGD 110,000 - 170,000; Full time

Job summary

A*STAR - Agency for Science, Technology and Research in Singapore seeks a Research Scientist in the Sustainability Directorate (IAIC) to advance computational catalysis through theory, ML, and high-throughput workflows.

The role focuses on integrating first-principles simulations with machine learning to accelerate catalyst design for decarbonisation, including CO₂ conversion and hydrogen-related reactions. Strong collaboration across disciplines is essential.

Qualifications

• PhD in Chemistry, Materials Science, Chemical Engineering or related discipline.
• Experience in computational heterogeneous catalysis and atomistic modelling.
• Strong programming skills in Python and experience with HPC.

Responsibilities

• Perform DFT calculations to study catalyst structures, adsorption energies, and reaction pathways.
• Develop and apply MLIPs for large-scale atomistic simulations with high accuracy.
• Design high-throughput screening workflows for catalyst discovery and optimisation.
• Collaborate with computational scientists, AI researchers, and experimental teams to translate insights.
• Collaborate with industry partners and affiliated research institutes.
• Publish findings in leading journals and present at major conferences.

Skills

Analytical thinking

Scientific curiosity

Problem solving

Written and verbal communication

Team collaboration

Python programming

High performance computing

Education

PhD in Chemistry, Materials Science, Chemical Engineering or related field

Tools

DFT software packages

Machine learning interatomic potentials

HPC environments

Job description

A*STAR - Agency for Science, Technology and Research in Singapore seeks a Research Scientist in the Sustainability Directorate (IAIC) to advance computational catalysis through theory, ML, and high-throughput workflows.

The role focuses on integrating first-principles simulations with machine learning to accelerate catalyst design for decarbonisation, including CO₂ conversion and hydrogen-related reactions. Strong collaboration across disciplines is essential.