PhD Positions in Machine Learning for Complex Quantum States (Quantum Physics, Quantum Computing, ML)

The collaborative research initiative “Machine Learning for Complex Quantum States” (MLCQS) is offering multiple fully funded PhD positions across eleven institutions in Germany and Switzerland, including the École Polytechnique Fédérale de Lausanne (EPFL). The initiative focuses on leveraging machine learning to address challenges in quantum simulation, quantum computing, and the study of complex quantum systems. Research topics include neural quantum states, quantum simulation algorithms, information dynamics of strongly interacting bosons, qutrit-based quantum computing, feedback control of quantum dynamics, and optimal readout of quantum simulators. The projects are supervised by leading academics such as Giuseppe Carleo (EPFL), Annabelle Bohrdt (LMU Munich), Markus Schmitt (Regensburg University), Christof Weitenberg (TU Dortmund), Monika Aidelsburger (MPQ, Garching), Marin Bukov (MPI PKS, Dresden), and Markus Heyl (Augsburg University). Applicants should have a strong background in quantum many-body physics, quantum optics, or quantum information, and a keen interest in applying machine learning to quantum systems. The positions are ideal for candidates with a master’s degree in physics or a related field, and experience with computational or experimental quantum research. The MLCQS consortium provides a vibrant, interdisciplinary environment with opportunities to collaborate across institutions and work on both theoretical and experimental projects. Funding covers stipend and tuition, with applications reviewed starting February 27, 2026. Interested candidates should send a CV and motivation letter to the contact email provided in the official call. Key research areas: machine learning, quantum physics, quantum computing, quantum simulation, quantum information, ultracold atoms, neural quantum states, quantum optics.