Research Fellow (Chemistry/ Chemical Engineering/ Materials Science)

Established in 2010, the Energy Research Institute @ NTU (ERI@N) is a pan-university research institute that focuses on systems-level research for tropical megacities. It performs translational research that covers the energy value chain from generation to innovative end-use solutions, motivated by industrialisation and deployment. ERI@N has multiple Interdisciplinary Research Programmes which focus on translational Research, Development & Deployment which focus on specific area of the energy value chain, and a number of Living labs and Testbeds which facilitate large scale technology deployment enabling validation and demonstration of real-world applications.

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We are looking for a Research Fellow to conduct Density Functional Theory (DFT) simulations and develop machine learning potentials to investigate zeolite-related systems. The role will focus on delivering research projects and promoting research excellence in this area.

The successful candidate will conduct Density Functional Theory (DFT) simulations and develop machine learning potentials to investigate zeolite-related systems, particularly under complex chemical environments. The candidate should be able to work independently while contributing effectively as part of a team. The candidate should possess strong analytical, technical, and problem-solving skills, as well as excellent scientific writing and presentation abilities. In addition, the candidate should be agile, adaptable, and eager to learn and develop new skills. The position offers a highly collaborative and dynamic research environment, with opportunities for close interaction within an interdisciplinary team.

Key Responsibilities:

• Conduct research outlined by PI

• Develop new research methodologies

• Critically analyse research data against theory

• Provide support to research students and other research staff

• Report research progress

• Publish research findings

Job Requirements:

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

• Strong background in computational modelling of surface chemistry and oxide materials

• Experience in developing and training interatomic machine learning potentials

• Experience in studying reactions and interactions at solid surfaces and liquidâ€solid interfaces using DFT simulations and machine learning potentials

• Experience with zeolite systems is highly desirable

• Proficiency in Python programming is an advantage

• Good written and oral communication skills

• Ability to work independently and good time management skills

If you share our interest in leveraging first-principles modelling and machine learning to solve challenging scientific problems and contribute to impactful discoveries, we warmly invite you to apply.

We regret to inform that only shortlisted candidates will be notified.