Describing geological processes on the nanoscale is challenging, partly because current molecular dynamics force fields for geologically relevant materials work well only within a moderate range of conditions. In this project, we aim at developing a systematic machine-learning based method for tailoring force fields to geologically relevant simulation conditions, to study the emergent properties robustly.
Frictional properties of materials in contact is sensitive to the structure of the contact interface. In this project, we will couple a physics-based simulator with a machine-learning based structure generation method to predict surface structures with a prescribed frictional behavior.
The nanoscale structure of a material determines its properties such as friction, wear or strength. In this project, you will use physics-based simulations to generate training data for machine learning methods that can be used to search for materials for sustainable energy application.
Diabetes can be cured by replacing dysfunctional beta cells by insulin-producing beta cells. This project will improve the differentiation of stem cells into insulin-producing beta cells by deep-learning based analysis of data from live cell imaging and gene and protein expression.
The project deals with studies and development of advanced algorithms for interacting many-particle systems (either classical or quantum mechanical ones) in condensed matter physics and/or subatomic physics with an emphasis on methods and/or algorithms from Quantum Computing theories.
The project deals with studies and development of advanced algorithms for interacting many-particle systems (either classical or quantum mechanical ones) in condensed matter physics and/or subatomic physics with an emphasis on Machine Learning, both supervised and unsupervised approaches.
Many cells can switch between mesenchymal and amoeboid migration modes. This project will combine live cell imaging and mechanistic modelling to find the physical factors driving the selection of migration modes in 3D hydrogel networks.
This project will contribute to the development of advanced materials for the treatment of non-healing bone defects. Numerical models based on controlled experiments will guide the material development.
This project is a theoretical and computational study of the swarming behavior of active particles in turbulent flows.
