The newly established research group in Phase Transition Engineering (Group Leader: Dr. Leif-Thore Deck) at the Max Planck Institute for Dynamics of Complex Technical Systems is inviting applications for the PhD Student (f/m/d) position.
Research topic: „Multi-scale Modeling of Biomolecular Phase Transitions“
The position is to be filled as soon as possible and is limited to three years.
Understanding how solids form from fluids is one of the most fundamental questions in the physical sciences. Solid assembly is pivotal to both natural and industrial systems, ranging from the manufacturing of crystalline products in the chemical and (bio)pharmaceutical industries to the formation of functional and toxic protein aggregates in living organisms. Deepening the understanding of biomolecular phase transitions therefore directly benefits both industrial and biomedical applications. On the industrial side, the insights gained will aid parallel projects aimed at improving the rational design of biopharmaceutical formulations and manufacturing processes that involve crystallization or precipitation for the purpose of purification. On the biomedical side, the project will contribute to elucidate how the transformation of disease-relevant disordered proteins (such as alpha-synuclein or tau) into toxic condensates or aggregates can be modulated.
Both liquid-liquid phase separation and solid formation of biomolecules are poorly understood from a theoretical point of view, providing many opportunities to make progress. The project comprises three elements that will be tackled in order of increasing complexity: first, predictive models for the thermodynamics of phase separation will be developed. The thermodynamic insights then will be used as basis for kinetic models, particularly of nucleation and growth. Taken together, thermodynamic and kinetic models then provide access to develop process models of manufacturing processes that involve phase separation and solid formation of biomolecules. All models will be experimentally validated using data generated in parallel projects, either relying on droplet microfluidics (for thermodynamics and kinetics), or on large scale setups (process models).
Knowledge and experience in mathematical modeling are advantageous.