Since 2025, Niklas Gebauer is a postdoctoral researcher in the machine learning group at TU Berlin, where he focuses on advancing machine learning methods for complex applications in the natural sciences. His primary expertise lies in the development of generative models for the accelerated exploration of chemical compound space, a crucial step in various fields such as drug discovery, renewable energies, catalysis, or nanotechnologies.

Niklas earned his PhD in the machine learning group as member of the BIFOLD graduate school and part of BASLEARN, the Berlin based joint lab of BASF and TU Berlin for machine learning. His work revolved around generative models for the inverse design of 3d molecular structures and deep neural networks predicting quantum chemical properties of molecules and materials. During his PhD, Niklas completed an internship at Google DeepMind as a student researcher to further hone his skills in cutting-edge machine learning techniques. Additionally, he has engaged in significant industrial collaborations with leading organizations in chemistry (BASF) and home appliances (BSH Hausgeräte). Prior to his PhD, Niklas studied computer science at the TU Berlin. His passion for machine learning developed during his Bachelor’s studies, where he wrote a thesis on representation learning in the robotics lab of Professor Dr. Oliver Brock. He then specialized in machine learning in his Master’s program, where his thesis with the machine learning group on neural networks for generating equilibrium molecules paved the way for his PhD research projects.