Topic: numerical modelling

Develop scientific AI methods for understanding and predicting plant-community dynamics in space and time within the PEPR DynaBiod programme. Process-informed neural networks will combine data with ecological mechanisms, allowing inference from incomplete, heterogeneous and noisy observations. The project examines mathematical formulations, learning architectures and the recovery of ecological processes and parameters. A relevant master’s or engineering degree, scientific Python, machine learning or numerical modelling, and scientific English are required. Appointment: 3 years; advertised star

The programme connects coastal-ocean processes with atmosphere–land interactions, monsoon variability and extreme weather, emphasizing the Indian Ocean while addressing wider climate questions. Participants combine observations, theory and numerical modelling to study hydrology, coastal feedbacks and prediction. Hands-on training runs January 18–25, followed by a research discussion meeting on January 27–29. The training targets graduate students and early-career scientists; the discussion meeting admits the broader relevant research community through selection based on research background and