Summary
Laura Rieger is a machine learning engineer and postdoctoral researcher with 11 years of experience applying data-driven methods to accelerate development of advanced battery materials and energy storage solutions. She specializes in explainability and uncertainty quantification for neural networks, translating complex models into transparent, actionable insights for high-stakes R&D. Her work spans academia and industry—from a PhD and visiting roles at DTU, UC Berkeley and Imperial College London to a machine learning engineering role at Veo Technologies—bridging fundamental research with deployment. Laura has demonstrated expertise in microstructure segmentation and exploratory use of large language models in materials science, highlighting a willingness to combine domain knowledge with cutting-edge AI. Fluent across deep learning, probabilistic methods and materials informatics, she shortens discovery timelines while maintaining model interpretability and trust. Based in Copenhagen, she brings a rare blend of rigorous academic training and practical engineering focus to translate ML innovations into real-world energy technologies.
11 years of coding experience
6 years of employment as a software developer
Bachelor of Science (B.Sc.), Computational Engineering Science, Bachelor of Science (B.Sc.), Computational Engineering Science at Technische Universität Berlin
Master of Science (M.Sc.), Computer Science, GPA 4.0, Master of Science (M.Sc.), Computer Science, GPA 4.0 at Korea Advanced Institute of Science and Technology
English, Danish, German