

Search by job, company or skills

Interested applicants are invited to apply directly at the NUS Career Portal. Please note your application will only be processed if you apply via NUS Career Portal.
NUS Career Portal link - https://careers.nus.edu.sg/job/Research-Assistant-%28Chemistry%29/34139-en_GB/st=D68832FA4BE5AADAE436B284622A4DE539804FB0
We regret that only shortlisted candidates will be notified.
The successful candidate will work with Asst Prof Ou Pengfei on developing and applying machine-learning interatomic potentials (MLIPs) for atomistic simulations of electrocatalytic interfaces and reaction mechanisms under a project on Machine-Learning Interatomic Potentials for Electrocatalytic Interfaces and Reaction Simulations.
The main responsibilities of the position include:
. Develop, train and validate MLIPs for electrode/electrolyte and catalyst/solution interfaces, including electrostatic and charge effects.
. Generate and curate DFT/AIMD reference data implement active-learning and uncertainty-guided data acquisition workflows.
. Perform large-scale molecular dynamics and reaction simulations to study interfacial structure, adsorption, solvent/electrolyte effects and reaction pathways.
. Benchmark MLIP predictions against first-principles calculations analyse results, maintain reproducible code/workflows, publish findings and collaborate with project partners.
Qualifications / Discipline:
. Bachelor's Degree in Computational Chemistry, Materials Science, Physics, Chemical Engineering, or a closely related field.
Skills:
. Strong skills in MLIPs and atomistic simulation DFT and molecular dynamics Python, Linux and HPC data-driven potential fitting and active learning.
. Knowledge of electrochemical interfaces, enhanced sampling and long-range electrostatics is desirable.
Experience:
. Demonstrated research experience in developing or applying MLIPs to molecular or condensed-phase systems, with evidence of independent research and scientific publication.
. Experience with electrolytes and/or interfacial systems is advantageous.
Job ID: 153312773
Skills:
Machine Learning, python, Bash, Amber, Schrödinger, gaussian, computational chemistry