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Short Description

An opening for a Research Scientist is available in the Sustainability Directorate at the A*STAR Institute of Advanced Intelligence and Computing (IAIC). We are seeking a highly motivated researcher with expertise in computational catalysis, atomistic modelling, and machine learning to develop next-generation computational tools for catalyst discovery.

Long Description

The successful candidate will conduct cutting-edge research in computational heterogeneous catalysis by integrating first-principles simulations, machine learning, and high-throughput computational workflows. The research will focus on understanding catalytic reaction mechanisms, predicting catalyst performance, and accelerating the design of catalytic materials for sustainable chemical processes, particularly those relevant to decarbonisation, including CO₂ conversion, hydrogen production and utilisation, and other energy-related reactions.

Key Responsibilities Include

  • Perform Density Functional Theory (DFT) calculations to investigate catalyst structures, adsorption energetics, reaction pathways, and surface chemistry.
  • Develop and apply machine learning interatomic potentials (MLIPs) to enable large-scale atomistic simulations with near first-principles accuracy.
  • Design and implement high-throughput computational screening workflows for catalyst discovery and optimisation.
  • Collaborate closely as a team with computational scientists, AI researchers, and experimental teams to validate predictions and translate computational insights into practical catalyst design.
  • Collaborate with industry partners, affiliated research institutes and other relevant stakeholders.
  • Publish research findings in leading international journals and present work at major scientific conferences.

The candidate should be capable of working independently while contributing effectively within a multidisciplinary research team. Strong analytical thinking, scientific curiosity, problem-solving ability, and excellent written and verbal communication skills are essential.

Technical Requirements

  • Ph.D. in Chemistry, Materials Science, Chemical Engineering or a related discipline.
  • Strong background in computational heterogeneous catalysis, surface science, or atomistic materials modelling.
  • Hands-on experience with Density Functional Theory (DFT) calculations using established electronic structure packages.
  • Hands-on experience in developing, training and applying machine learning interatomic potentials (MLIPs) or other atomistic machine learning models.
  • Experience in studying catalytic reaction mechanisms, transition-state searches, and microkinetic modelling.
  • Experience in high-throughput materials screening workflow automation.
  • Strong programming skills in Python and familiarity with scientific software development.
  • Experience with high-performance computing (HPC) environments.

If you are passionate in integrating first-principles modelling and machine learning to solve challenging scientific problems and contribute to impactful discoveries in materials, we warmly invite you to apply. Please note that only shortlisted candidates will be notified.

Competencies

Working with Others

Possesses a strong background in relevant academic / research field

Demonstrates critical thinking and problem solving skills

Displays clear understanding of the proposed field of study

Effective Communication

Exhibits a high level of motivation in a STEM career

Innovativeness

Integrity