Summary
Jiri Hostas is a research scientist and machine-learning practitioner with over eight years applying data-driven techniques at the intersection of physics, chemistry, and materials discovery. Currently a Research Officer at the National Research Council Canada, he focuses on quantum-enhanced design for materials and chemistry after leading ML-driven materials and molecular design projects as a postdoc. He has introduced and combined methods from autoencoders and VAEs to normalizing flows, active learning and Gaussian processes to accelerate geometry optimization and catalyst screening by orders of magnitude. Experienced in HPC workflows, patent and literature mining, and industrial collaborations, he bridges academic rigor with practical, client-facing problem solving. He also mentors students and helped secure grant funding while publishing in high-impact journals, demonstrating strong communication and project leadership. Outside work he pursues navigation-rich outdoor adventures across ranges from the Canadian Rockies to the European Alps, reflecting a taste for complex problem solving both in the lab and the wild.
8 years of coding experience
11 years of employment as a software developer
Secondary school, Secondary school at Gymnazium Ostrava-Zabreh
Bachelor's degree, Chemistry, Bachelor's degree, Chemistry at University of Chemistry and Technology in Prague (UCT Prague)
Doctor of Philosophy - Ph.D., Chemistry, Doctor of Philosophy - Ph.D., Chemistry at Charles University in Prague
Czech, English