Milan Harth

Milan Harth serves as a Lecturer at the Technical University of Munich within the Simulation of Nanosystems for Energy Conversion group, part of the TUM School of Computation, Information and Technology. His work focuses on the intersection of computational methods and energy materials research.

His research interests span machine learning applications for materials science, particularly in perovskite solar cells and nanoelectronics. He specializes in combining computational modeling with experimental approaches to optimize materials for energy conversion applications, with particular expertise in machine vision techniques for material characterization and quantum dot synthesis.

His publication record shows a consistent focus on applying machine learning to materials discovery and characterization, with recent work emphasizing data-efficient optimization methods and computer vision techniques for analyzing perovskite materials. The research demonstrates strong interdisciplinary connections between computational physics, materials engineering, and artificial intelligence.

Teaching responsibilities include Computational Methods for Nanoelectronics, PhD seminars in SNE (Simulation of Nanosystems for Energy Conversion), and the Seminar on Quantum Engineering and Machine Learning, indicating his role in training the next generation of computational materials scientists.