neural quantum states

The group of Dario Poletti at the Singapore University of Technology and Design and at the Centre for Quantum Technologies has an opening for Research Fellow for 2 years (with possible extension).

The main research direction is many-body quantum systems out of equilibrium. Particular emphasis will be given to numerical simulations, e.g. tensor networks and/or neural quantum states, and potential collaboration with experimental groups. We are interested in quantum chaos, quantum thermodynamics, quantum transport, entanglement transitions etc.

I’m looking for one postdoc in Quantum/Condensed Matter Numerical Physics to join our group between École Polytechnique, INRIA Saclay and College de France.

The position is to work on Variational Monte Carlo / Neural Quantum States methods and apply them to (preferentially) electronic structure problems (Hubbard Model, Quantum chemistry...), ideally by leveraging foundation models. Both candidates that wish to work on the methodological aspect or on the applications are welcome.

The development of quantum technologies and machine learning are two of the most dynamic research directions of our time. Within the Helmholtz Young Investigator Group “Machine Learning for Quantum Technology” we aspire to merge the parallel advancements of both fields, where quantum challenges match the natural strengths of machine learning and, reversely, the quantum applications call for the development of new machine learning techniques. Therefore, our research targets the exciting intersection of (non-equilibrium) quantum matter, quantum information, and machine learning with the goal of unveiling previously unexplored many-body physics and devising interactive strategies to manipulate artificial quantum systems. The Young Investigator Group is part of the Peter Grünberg Institute - Quantum Control (PGI-8) at Forschungszentrum Jülich, which specializes in novel optimal control strategies for emerging quantum technologies. Current research directions include tensor network methods, machine learning, in-situ optimal control, and quantum many-body phenomena.