Summary
Rangsiman Ketkaew is a postdoctoral researcher at ETH Zürich with nine years of experience at the intersection of theoretical chemistry, molecular simulation, and machine learning. He developed collective variables from electronic structure and statistical techniques during his PhD at the University of Zurich, and has hands-on expertise in ab initio molecular dynamics, enhanced sampling, and GPU-accelerated scientific code (OpenACC/OpenMP). His background spans industry and academia—building ML force fields for drug discovery, benchmarking MD software like GROMACS, and designing scalable AWS clusters—so he bridges fundamental theory with production-oriented tooling. Comfortable both teaching physical chemistry and writing technical content for cloud audiences, he brings clear science communication to complex computational workflows. An analytical thinker who blends symbolic regression and autoencoders with electronic-structure insights, he focuses on turning high-dimensional quantum data into actionable collective variables for efficient simulation.
9 years of coding experience
5 years of employment as a software developer
Master’s Degree, Computational chemistry, Master’s Degree, Computational chemistry at Thammasat University
Doctoral Degree, Theoretical Chemistry, Doctoral Degree, Theoretical Chemistry at University of Zurich
Thai, English, German