Assistant Professorship Machine Learning in Process Engineering

JP Dr.-Ing. Fabian Jirasek

In this Assistant Professorship, the exploitation of Machine Learning methods in Process Engineering is explored. The possible areas of application are manifold, ranging from the prediction of thermodynamic properties of pure components and mixtures, the development of optimal experiment design using Active Learning, and the automated monitoring and control of chemical processes and plants to the fundamental knowledge discovery from (process) data.

Assistant Professorship Electrolyte Thermodynamics and Molecular Simulation

JP Dr.-Ing. Maximilian Kohns

The field of Electrolyte Thermodynamics covers the measuring and modeling of thermodynamic properties of electrolyte solutions as well as their application for process design. Molecular Simulation is a versatile tool based on molecular thermodynamics, which allows for predicting the system behavior in a wide variety of applications, requiring only little information to parameterize the models.