PhD fellowship in Machine Learning for Environmental Sciences

We are looking for a creative student who is interested in contributing to the mission of the Global Wetland Center, by developing novel machine learning methods to model greenhouse gas fluxes from both remotely sensed multimodal data and ground-level measurements. To overcome challenges of limited reference data, the student will work on hybrid modelling combining process-based models and deep learning, as well as self-supervised learning approaches.

ELLIS Edge Newsletter
Join the 6,000+ people who get the monthly newsletter filled with the latest news, jobs, events and insights from the ELLIS Network.