COURSES
- Physics Informed Machine Learning (master course)
- Effective Theory of Deep Learning (PhD course)
- Tensor Networks for Machine Learning (PhD course)
- Quantum Machine Learning (master course)
- Computer Science for Physicists (undergraduate)
MASTER PROJECTS
- Tensor networks approach to ARC challenge
- Deep learning for quantum compilation
- Discovering patterns in quantum algorithms with ZX calculus and deep learning
- Tensor networks for generative modelling of medical data
- Next-generation reservoir computing and tensor networks
- Physics informed machine learning for sound modelling
- Variational adiabatic classical computing
- Tensor network programming
PHD POSITIONS
PhD positions are available. Several research areas are possible, including:
- Quantum reservoir computing (including numerical simulations, theory, and experiments (cold atoms and muons))
- Adiabatic quantum computing and the development of novel quantum algorithms
- Measurement-based quantum computing (connection to dequantization and use of tensor networks)
- Application of tensor networks in machine learning (e.g. the TN approach to the ARC challenge)
- Exploring neural quantum states in the context of many-body quantum dynamics
- Utilizing machine learning approaches (such as autoencoders, SINDY, and Koopman theory) for an effective description of many-body quantum states