Job Description

The successful candidate will work with Asst. Prof. Ou Pengfei on developing and applying advanced computational methods - such as ab initio molecular dynamics (AIMD), machine learning force fields (MLFFs), and microkinetic modeling (MKM) to investigate catalyst activity, stability, and degradation under a project on From Laboratory to Industrial Operating Conditions in Electrocatalysis.

The main responsibilities of the position include:
. Perform ab initio molecular dynamics (AIMD) simulations to study high-temperature stability of catalysts.
. Develop and fine-tune machine-learned interatomic potentials based on AIMD data for accelerated large-scale MD simulations.
. Conduct DFT-based activation barrier calculations and integrate with microkinetic modelling to predict electrochemical reaction trends under varying pH, potential, and temperature conditions.
. Collaborate closely with experimental partners for model validation and iterative theory-experiment feedback.
. Draft research publications and reports contribute to grant reporting obligations.

Qualifications / Discipline:
. Ph.D. in Computational Chemistry, Materials Science, Chemical Engineering, Condensed Matter Physics, or a closely related field.

Skills:
. Strong expertise in density functional theory (DFT), AIMD, or related first-principles simulation methods (experience with VASP, CP2K, LAMMPS, or similar codes preferred).
. Experience in machine learning for materials modelling.
. Familiarity with electrochemistry, electrocatalysis, or energy materials is a strong plus.
. Good programming/scripting skills (Python, Bash, etc.).
. Excellent written and oral communication skills.

Experience:
. Demonstrated experience in performing and analysing first-principles simulations of materials.
. Prior exposure to electrochemical modelling and/or catalysis is desirable.
. Experience in collaborative research, particularly theory-experiment interaction, is advantageous.
. A strong publication record in relevant journals is expected.

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