Research Assistant (Chemistry)

Interested applicants are invited to apply directly at the

Your application will be processed only if you apply via

We regret that only shortlisted candidates will be notified.

Job Description

The successful candidate will work with Asst. Prof. Ou Pengfei on CO2 electroreduction mechanisms and catalyst design using first-principles simulations under a project on CO2-to-n-propanol conversion on molecular electrocatalysts enabled by graph theory and machine learning.

The main responsibilities of the position include:
. Perform density functional theory (DFT) calculations to investigate reaction mechanisms and energetics of CO2 electroreduction on molecular electrocatalysts.
. Construct and analyze reaction networks using graph-theory-based approaches.
. Apply machine learning methods to assist in reaction pathway discovery and catalyst screening.
. Analyze and interpret computational results and assist in preparation of research reports and manuscripts.

Qualifications / Discipline:
. Master's degree in Chemistry, Chemical Engineering, Materials Science, or a related field, with a background in computational chemistry, catalysis, or theoretical materials science.

Skills:
. Knowledge of density functional theory (DFT) and atomic simulations, as well as programming and data analysis skills (e.g., Python), and experience in scientific computing and high-performance computing environments. Strong analytical and problem-solving abilities, and effective scientific communication skills.

Experience:
. Research experience in computational chemistry, electrocatalysis, or heterogeneous catalysis, including performing DFT calculations and analyzing catalytic reaction mechanisms. Experience in scientific data analysis and visualization, and the ability to participate in writing research reports, presentations, or publishing papers.