The posting
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%29/34575-en_GB/?st=671181AD041C8E761C106910B9677CE06FB65BC0
We regret that only shortlisted candidates will be notified.
Job Description
The successful candidate will work with Assistant Professor Ou Pengfei on computational and data-driven catalyst modelling under the MOE AcRF Tier 1 project “Nitrogen Activation at Catalyst Surfaces to Catalyze Net-Zero: An AI-Driven Approach.”
The project aims to understand and predict nitrogen activation and transformation at catalyst surfaces by integrating first-principles calculations with data-driven and interpretable machine-learning approaches. The candidate will contribute to the development of computational models and datasets that connect catalyst structures and local chemical environments with adsorption energetics, reaction pathways, and catalytic performance.
The main responsibilities of the position include: • Perform density functional theory (DFT) calculations to investigate adsorption structures, reaction energetics, and elementary reaction pathways involving nitrogen-containing intermediates on catalyst surfaces. • Construct, curate, and analyse computational datasets of catalyst structures, adsorption energies, and reaction energetics. • Develop physically meaningful descriptors and apply machine-learning methods to identify structure–property and structure–reactivity relationships. • Apply interpretable machine-learning approaches, such as symbolic regression and feature-attribution analysis, to identify key chemical and structural factors governing catalytic performance. • Use computational and data-driven models to screen catalyst candidates and propose promising compositions or local active-site environments for further investigation. • Analyse computational results, prepare figures and reports, and contribute to research publications and presentations. • Work collaboratively with other group members and experimental collaborators to validate computational predictions and refine catalyst-design strategies.
Qualifications / Discipline:
• Bachelor’s degree or higher in Chemistry, Materials Science, Chemical Engineering, Physics, Computational Science, or a closely related discipline. • Training in computational chemistry, materials modelling, catalysis, or data-driven materials research would be advantageous.
Skills: • Knowledge of density functional theory and atomistic modelling. • Familiarity with first-principles simulation software such as VASP and related computational analysis tools. • Programming and data-analysis skills, preferably using Python. • Familiarity with machine-learning methods for materials or chemical applications. • Experience with interpretable machine-learning approaches, descriptor development, symbolic regression, SISSO, SHAP, or related techniques would be advantageous. • Good analytical, problem-solving, scientific writing, and communication skills. • Ability to work independently as well as collaboratively within a multidisciplinary research team.
Experience: • Prior research experience in computational catalysis, computational materials science, electrocatalysis, or related areas. • Experience in calculating and analysing adsorption energetics, reaction pathways, or structure–property relationships using DFT. • Experience in developing or applying machine-learning models to chemical or materials datasets. • Experience with interpretable machine learning, physical descriptor construction, and data-driven catalyst screening would be particularly relevant.
