The Department of Ecology, Environment and Plant Sciences invites applications for a postdoctoral researcher to develop a computational digital twin of phytoplankton communities. The project integrates eco-evolutionary theory, high-frequency observational data, and machine learning to infer fitness and growth functions under changing environmental conditions. Responsibilities include eco-evolutionary modelling, SciML applications, integration of dynamical systems with ecological time-series data, interdisciplinary collaboration, and publication of results. The position emphasizes computational and theoretical work with close interaction with empirical datasets.