Welcome to the "Between Models and Reality" Ph.D. school on Machine Learning in physics. The focus of this school is the application of Machine Learning to physics research and the many challenges and solutions that this entails. The focus is mainly on application, and will not only consist of lectures.
So if you are a Ph.D. student in a field of physics (or a related "large research data" science), who would like to learn more about both basic but also more advanced Machine Learning approaches and ways of thinking, don't hesitate to apply.
The School is held in collaboration between the 4EU+ alliance of universities (Prague, Heidelberg, Paris (Panthéon-Assas and Sorbonne), Copenhagen, Geneva, Milan, Warsaw) and the AI-Physics Marie Curie Ph.D. network, but is open to all.
The school will be held in the famous Auditorium A at the Niels Bohr Institute in central Copenhagen. There is room for 60 students, which will be accepted on a first-come-first-serve basis, provided that the students fulfills the requirements and submits a well motivated registration.
Lectures will be run by a diverse group of physicists and computer scientists, led by the world-class researchers Tilman Plehn (Univerity of Heidelberg) and Thea Aarrestad (ETH Zurich), on topics such as:
- Bayesian neural networks
- Uncertainty quantification & learning
- Generative machine learning
- Representation learning
- Fast machine learning
- Physics-informed neural networks
