PhD Position - Molecular Simulation and Machine Learning for Predictive Chromatography Modeling | Chemieingenieurwesen (m/w/d)

Job Summary

This PhD position, embedded within the Helmholtz Graduate School for Data Science in Life, Earth and Energy (HDS-LEE), focuses on developing predictive models for chromatography, a crucial process in bioprocess development. The successful candidate will combine molecular simulations, protein structure descriptors, and machine learning techniques to predict ion-exchange isotherm parameters directly from molecular properties. These predictions will be integrated into the open-source CADET simulation framework, enabling fully predictive process simulations without extensive experimental calibration. Day-to-day tasks involve developing molecular descriptors, designing and training QSPR/machine learning models, simulating elution processes, and collaborating with industrial partners. This interdisciplinary role requires a Master’s degree in a relevant engineering or computational science field, proficiency in a programming language like Python, and strong analytical skills, offering a unique opportunity to contribute to open-source software and industrially relevant bioengineering applications.