The Integrative Food Systems Analysis group at Leibniz-LSB@TUM seeks a PhD student to develop computational approaches for analyzing food-related multi-omics data. The project focuses on graph-theoretic and graph-machine-learning methods to characterize and predict food–effector systems. The successful candidate will design and benchmark algorithms, analyze large-scale omics datasets, and contribute to interdisciplinary research at the interface of bioinformatics, systems biology, and food science. This position offers training in cutting-edge computational biology within a leading non-university research institute.