PhD position in soft matter physics/colloid chemistry/machine learning at the Charles University Prague

Open PhD thesis project in the Biomembrane Remodeling group at the Faculty of Mathematics and Physics of Charles University Prague.

The long term goal of the group is to develop and implement accurate and efficient methods for the simulation of biological systems at the cellular scale. Our approach is to insert molecular specificity into a continuum theoretical description of the system by extracting local material properties from accurate molecular simulations. We have recently developed such methods for biomembrane elasticity, as membranes form the most important elements of the substructure in cells. The next step is to extend our methodology to include detailed terms for protein-membrane interaction as well as a more sophisticated description of electrostatics and solvation.

Recent progress in the analysis of machine learning methods suggests the possibility to create a range-separated mean field theory of the energy. If a sufficient number of local environments is sampled, the mean-field theory will describe both nonlocal electrostatic interactions and local interactions. The subject of the thesis is develop such a theory and collect the necessary data from molecular dynamics simulations.

Motivated individuals with an interest in theoretical bio- or soft matter physics as well as physical or colloid chemistry are encouraged to apply. Candidates eligible for the PhD position should have a MSc degree at the start of employment; previous experience in soft matter theory or molecular dynamics simulations is advantageous but not required.

The positions include funding for conferences and travel. No teaching will be required. The positions are available immediately, applications will be reviewed until a position is filled.

The candidates interested in a PhD position (3 years) should submit their cover letter and CV.  Applications and inquiries should be sent directly to the PI at allolio@karlin.mff.cuni.cz , https://www2.karlin.mff.cuni.cz/~allolio/.