Research Engineer - MOF Computational Modeling ML Potentials

National University of Singapore; Singapore; On-site; SGD 56,000 - 100,000; Full time

Job summary

The National University of Singapore invites applications for a research position to investigate microscopic properties of guest molecules in nanoporous materials, focusing on metal-organic frameworks. The successful candidate will contribute to a computational project addressing adsorption and diffusion phenomena and will work within a collaborative team.

Responsibilities include calculating interaction energies with density-functional theory, training machine-learning models and potentials,

Qualifications

• Bachelor or Master degree in Chemical Engineering, Chemistry, Physics, or related discipline.
• Strong background and hands-on experience in molecular simulation, machine learning, and density-function theory calculations.
• Knowledge of nanoporous materials, particularly metal-organic frameworks.
• Strong analytical and problem-solving skills.
• Ability to work independently, as well as team-work ability.
• Good written and spoken English communication skills.

Responsibilities

• Calculate interaction energies from density-functional theory.
• Train machine learning models/potentials.
• Conduct atomistic simulations to explore adsorption/diffusion.
• Analyze computational results.

Skills

Molecular simulation

Machine learning

Density-functional theory

Analytical thinking

Teamwork

English communication

Education

Bachelor or Master degree in Chemical Engineering, Chemistry, Physics, or related discipline

Job description

The National University of Singapore invites applications for a research position to investigate microscopic properties of guest molecules in nanoporous materials, focusing on metal-organic frameworks. The successful candidate will contribute to a computational project addressing adsorption and diffusion phenomena and will work within a collaborative team.

Responsibilities include calculating interaction energies with density-functional theory, training machine-learning models and potentials,