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.

Requirements

  • 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.

Supervisors

Professor Morten Hjorth-Jensen

Researcher Simen Kvaal

Call 2: Project start autumn 2022

This project is in call 2, starting autumn 2022. 

 

Published Aug. 20, 2020 12:32 PM - Last modified Nov. 17, 2020 3:26 PM