The student will work with the data from the [MET Norway](https://www.met.no/en) weather API, which provides high-resolution weather forecasts and historical observations. Once the data is prepared and pre-processed by building a spatiotemporal graph representation, the student will implement and fine-tune Spatiotemporal Graph Neural Networks (STGNNs) models using PyTorch and [Torch Spatiotemporal](https://torch-spatiotemporal.readthedocs.io/en/latest/), experimenting with different architectures and hyperparameters. By the end of the project, the student will have developed a strong understanding of meteorological data pre-processing, graph-based modeling, spatiotemporal deep learning, and its application to real-world weather forecasting tasks, which can be further explored in future research, as well as potential experience in deploying these models in production environments.