Research Fellow (Chemistry)

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-Fellow-%28Chemistry%29/33835-en_GB/?st=7CDD47F93D7F4AA683ED12119F4A4E3298BD528A We regret that only shortlisted candidates will be notified. Job Description The successful candidate will join Asst. Prof. LI Xiaoyan’s group to advance research in AI-accelerated multiscale simulation and modeling for CCUS. The main responsibilities of the position include: • Kinetically simulate the reconstruction of metal oxides and alloying under the reaction conditions • Involve AI/ML method into kinetic Monte Carlo model • Publish research findings in high-impact journals and present at leading international conferences. • Contribute to proposal writing and assist in preparing funding applications in support of group research activities. Qualifications / Discipline: • PhD in Chemical Engineering, Chemistry, Materials Science, Physics, Computational Science, or a related discipline. • Proven research track record, evidenced by publications in peer-reviewed journals. • Excellent communication skills and strong interest in interdisciplinary collaboration. Skills: • Expertise in computational materials science, including atomic kinetic mente carlo, molecular simulations, density functional theory (DFT), or other multiscale methods. • Strong programming skills (Python, C/C++, MATLAB) with experience in AI/ML methods, such as MLIP. • Skilled in scientific writing and communication, with experience preparing manuscripts for high-impact journals and delivering research presentations at international conferences. Experience: • Know about the structure transformation in the interface of nanomaterials under the reaction conditions • Strong experience in machine learning and AI-driven modeling for scientific applications, such as machine-learned interatomic potentials and enhanced sampling approaches. • Collaborated across computational and experimental teams to validate predictions and deepen understanding of structure–property relationships.