Summary
Karin Hrovatin is a bioinformatics scientist with six years’ experience at the interface of machine learning and biology, currently translating advanced ML methods into industrial applications at Merck in Darmstadt. She focuses on computational protein design and protein language models, experimental design via Bayesian optimization and transfer learning, and developing open-source Python tools that move research into production. Her training—an MSc with distinction from Edinburgh, a PhD at Helmholtz/TUM, and an MIT exchange on scRNA‑seq translation—gives her deep methodological rigor plus hands‑on translational expertise. Beyond core research she coaches innovation programs, manages web and software tooling, and runs a data‑science communication practice that intentionally surfaces what didn’t work, blending wet‑lab experience, product delivery and public-facing storytelling.
7 years of coding experience
1 year of employment as a software developer
Master's degree, Bioinformatics, With Distinction, Master's degree, Bioinformatics, With Distinction at The University of Edinburgh
LISEAD, LISEAD at ESMT Berlin
Bachelor's degree, Biotechnology, Average grade: 9.93 out of 10; Dissertation grade: 10 out of 10, Bachelor's degree, Biotechnology, Average grade: 9.93 out of 10; Dissertation grade: 10 out of 10 at University of Ljubljana, Faculty of Biotechnology
Mathematical class, Mathematical class at High School Bežigrad