We are seeking individuals with a curiosity-driven research record who work at the intersection of machine learning (ML) and quantum many-body physics. We are also happy to work with experts in one of the two fields who are committed to learning the other. Moreover, we look for interest in developing interpretable, reliable, and scalable ML methods, neural quantum states, understanding the simplicity bias of overparameterized neural networks, or applying them to quantum systems, such as ultracold quantum simulators.
In this position, you will be encouraged to identify and tackle important challenges in quantum simulators, taking advantage of existing collaborations with leading experimental groups. You may join (and lead) existing collaborations aiming at scalable and practical Hamiltonian learning and detecting quantum phases of matter and their order parameters in exotic systems. You may he...