-
algorithms to compute similarity between interaction interfaces across millions of comparisons. This hinders identification of novel modes of protein binding, i.e. those predicted by AlphaFold, and it hinders
-
substrates while advancing our understanding of deep learning through dynamical systems theory. You will work with two cutting-edge experimental systems: (1) light-controlled active particle ensembles
-
techniques. These surrogates will be tuned for rapid, uncertainty‑aware predictions and integrated into decision‑support tools for deep geological repositories of nuclear waste—one of the most pressing
-
approaches in the powerful model systems zebrafish and fruit fly, and structural biology (including AlphaFold predictions and cryo-EM), we will dissect the roles of these novel mRNA export regulators and
-
. Here, we use symmetry and the geometric properties of the molecules in order to calculate bounds that help to predict specific behavior. Moreover, we would like to more widely explore the possibility
-
heterogeneous and opportunistic sensor networks. Therefore, such an approach may significantly improve rainfall and runoff predictions. Research goals: Our primary goal is to improve the accuracy and prediction
-
formation of two- and three-dimensional DNA structures which self-assemble from a number of interacting single-stranded DNA molecules. An accurate prediction of DNA structures still remains difficult, which
-
Jülich who are leaders in their respective fields, viz. AI-driven materials property prediction and high-throughput materials development. Computational studies will be performed on Jülich’s world-class
-
of the Earth system at different temporal and spatial scales to improve predictive capability. Comprehensive education: Enjoy numerous opportunities for scientific training, skills development and problem
-
separately, yet a reliable, open-source tool integrating a shallow-water solver and a multiphase porous-media solver within the same framework is missing. Without this coupling, it is not possible to predict