Development of Machine Learning Algorithms for studies of quantum mechanical many-body systems
The project deals with studies and development of advanced algorithms for interacting many-particle systems (either classical or quantum mechanical ones) in condensed matter physics and/or subatomic physics with an emphasis on Machine Learning, both supervised and unsupervised approaches.
Plots of one-body densities for two-dimensional quantum dots for 2, 6 and 12 electrons computed with Reduced Boltzmann Machines (RBM) and standard Variational Monte Carlo approaches for fermions.
There is also the possibility to combine quantum computing approaches with machine learning algorithms.
- The applicant is expected to hold a Master’s degree or equivalent in Computational or Theoretical Physics with specialization in quantum mechanical many-body problems with a strong background in Computational Physics/Science.
- Candidates with a background in Machine Learning will be preferred.
Call 2: Project start autumn 2022
This project is in call 2, starting autumn 2022.