Summary
Robert Raddi is a postdoctoral researcher and theoretical chemist with eight years of experience applying Bayesian inference, statistical physics, and high-performance computing to problems in computational structural biology and drug discovery. He has published extensively over the past five years while progressing from graduate research assistant to adjunct research faculty and now a postdoc at UCSF, demonstrating both deep research skills and sustained productivity. Proficient in Python, Bash, and HPC workflows, he develops simulation-analysis pipelines and custom tools to probe biomolecular thermodynamics and kinetics. A committed teacher and mentor, he has earned a teaching award and helped run a hands-on computing and statistics workshop, showing an aptitude for translating complex methods for learners. Colleagues appreciate his blend of rigorous quantitative modeling and practical scripting that turns large simulation datasets into actionable insight.
9 years of coding experience