Computational quantum many-body physics – Postdoc at Forschungszentrum Jülich

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.