Developing Equivariant Networks

1 day ago


Umeå, Västerbotten, Sweden Euro Math Soc Full time
Research Focus

Geometric deep learning is a rapidly growing field that combines algebraic geometry with machine learning. Our research focuses on developing new mathematical theories for understanding and constructing equivariant networks. These networks react properly to symmetries in data or learning tasks, leading to more efficient and accurate models.

We are exploring new ways to construct these networks, describe their resulting models mathematically, and analyze how symmetries affect the training process. This research has the potential to significantly impact the field of artificial intelligence and beyond.

If you are passionate about theoretical foundations of AI and want to contribute to groundbreaking research, apply now We welcome highly motivated and talented individuals who share our enthusiasm for advancing the state-of-the-art in geometric deep learning.



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