Principal Investigator

Esther Heid

Assistant Professor · Machine Learning for Sustainable Chemistry · TU Wien

🏆 ERC Starting Grant · 2024 🏆 FWF START Prize · 2024

Esther Heid is an Assistant Professor for Machine Learning for Sustainable Chemistry in the Faculty of Technical Chemistry, TU Wien, Austria, and head of the TU-wide dataTUdiscovery AI/ML service center. She received her Ph.D. in Theoretical Chemistry from the University of Vienna in 2019. She subsequently held postdoctoral positions at MIT (2020–2022) and TU Wien (2022–2024), where she advanced her work at the interface of chemistry and machine learning. In 2024, she was awarded a prestigious ERC Starting Grant along with an FWF START Prize, and started her own research group at TU Wien.

As a scientist, teacher, and mentor, Esther Heid works at the interface of machine learning and chemistry, using computers to simulate, predict, and optimize chemical processes. Her current research focuses on machine-learned reaction property prediction, drawing on graph theory, cheminformatics, and deep learning to better understand chemical reactions and, in the longer term, to develop computational approaches for the eco-friendly synthesis of a wide range of target molecules.

She also develops and maintains scientific software, including a Python package that predicts molecule and reaction properties with graph-convolutional neural networks. Her further expertise spans the simulation of soft matter and the quantum-mechanical calculation of molecular and atomic properties.