Conducting research for a changing society: this is what drives us at Forschungszentrum Jülich. As a member of the Helmholtz Association, we aim to tackle the grand societal challenges of our time and conduct interdisciplinary research into a digitalized society, a climate-friendly energy system, and a sustainable economy. Work together with some 7,600 employees in one of Europe’s biggest research centres and help us to shape change!

The Bioinformatics Division of the Institute of Bio- and Geosciences - Bioinformatics (IBG-4) processes and develops methods and algorithms to achieve a fundamental understanding of high-dimensional data and processes in the bioeconomy in particular. Bioinformatics at Forschungszentrum Jülich plays a leading role at the international level, for example in the field of plant and microbial data management, in the evaluation of new methods of genome analysis, in the integration, interpretation, and visualization of high-dimensional omics data from the field of bioeconomics, and in the modeling, simulation, and engineering of biomolecular systems, including enzymes.

Apply your data science skills to real-world challenges!
At the Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE), we train the next generation of data scientists to tackle key global issues in domain sciences such as life, earth or energy. Learn more at www.hds-lee.de Institute specific promise here.

We are looking to recruit a

PhD position - Molecular simulation and machine-learning for predictive chromatography modeling with CADET - within the HDS-LEE graduate school Chromatography modeling, while crucial for modern bioprocess development, still heavily relies on empirical determination of key model parameters. By combining protein structure descriptors, molecular simulations, and machine learning, this PhD project seeks 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. Embedded in the Helmholtz Graduate School for Data Science in Life, Earth and Energy (HDS-LEE), the project offers an interdisciplinary research environment at the interface of bioengineering, computational biophysics, and data-driven modeling, with strong links to open-source software development and industrially relevant applications. Tasks include:

Development of molecular descriptors from protein structures and simulations
Design and training of QSPR and machine learning models to predict ion-exchange isotherm parameters
Integration of predicted parameters into the CADET chromatography simulation framework
Simulation and analysis of batch and gradient elution processes using predictive isotherms
Curation and analysis of experimental chromatography data for model training and validation
Collaboration with experimental and industrial partners
Dissemination of results through high-quality publications and open-source software contributions

Master’s degree in chemical engineering, biotechnology, computational biophysics, bioinformatics, data science, or a closely related discipline with a strong academic record
Genuine interest in data-driven and physics-based modeling, molecular simulations, and their application to bioprocesses and bioseparations
Proficiency in at least one programming language, preferably Python; experience with scientific computing, numerical modeling, or machine-learning frameworks is an asset
Strong analytical skills with a solid understanding of data evaluation, modeling, and interpretation of complex datasets
Ability to work independently as well as collaboratively in an interdisciplinary and international research environment
Very good written and oral communication skills in English; knowledge of German is beneficial but not required
High motivation for academic development, demonstrated by academic...