The Nonequilibrium quantum dynamics group (https://www.pks.mpg.de/nqd ) at the Max Planck Institute for the Physics of Complex Systems (https://www.pks.mpg.de/) in Dresden (Germany) is looking to hire a PhD student to work at the intersection of quantum many-body physics and reinforcement learning (RL).

The successful applicant will become part of the structured International Max Planck Research School (IMPRS) "Quantum Dynamics and Control" (https://www.imprs-pks.mpg.de/), which offers additional PhD training benefits via its scientific network. They will be supervised jointly by Marin Bukov (MPI-PKS, Dresden) and Markus Schmitt (University of Regensburg).

The research project encompasses the design of reinforcement learning algorithms tailored to control quantum many-body systems using tensor network techniques, for more information see Nature Machine Intelligence 5, 780–791 (2023) (https://www.nature.com/articles/s42256-023-00687-5). You will develop a new generation of deep learning architectures for RL agents based on physics-inspired tensor networks (e.g., matrix product states, operators, etc.), that learn to control the collective behavior of many interacting quantum degrees of freedom; applications to physical problems include automatizing the preparation of states in Hubbard- and Heisenberg-type models on quantum simulators and quantum computers featuring symmetry-breaking or topological order.

Your job:

• design reinforcement learning algorithms for quantum many-body systems based on tensor networks;
• simulate numerically non-equilibrium dynamics with connection to experiments with quantum simulators;
• present your research results to the global scientific community in international conferences in Europe and abroad;
• write scientific articles and project reports;
• tutor/co-supervise bachelor and master students;

Your Profile:

• masters (or equivalent) degree in physics or computer science;
• strong interest in numerical methods for quantum many-body physics, preferably with hands-on experience;
• coursework and/or research experience in (a subset of) quantum mechanics, quantum optics, condensed matter physics, statistical physics, non-equilibrium quantum many-body physics, computational physics, machine learning for physicists;
• strong programming skills, ideally including experience in deep learning and/or high performance computing;
• working proficiency in English – both verbal and written;
• ability to shape your research project according to your own interests;

Our Offer: we work on the very latest challenges at the interface of two of the most exciting technological developments of our time -- machine learning and quantum technology -- and are offering you the opportunity to actively participate! We offer ideal conditions for you to complete your doctoral degree:

• access to outstanding scientific and technical infrastructure and the state-of-the-art computing facilities of the Max Planck Society (Germany's premier, non-university research organization dedicated to cutting-edge research, https://www.mpg.de/en);
• opportunities to participate in international schools and conferences, and exposure to biweekly workshops onsite via MPI-PKS's visitors program (https://www.pks.mpg.de/events/workshops-seminars): ideal for expanding your scientific horizon and building your own scientific network;
• opportunity to join the prestigious International Max Planck Research School (IMPRS) "Quantum Dynamics and Control" (https://www.imprs-pks.mpg.de/);
• world-class PhD training and regular supervision by Marin Bukov and Markus Schmitt;
• networking opportunities with a large pool of highly motivated PhD students/postdocs/group leaders in an international and interdisciplinary working environment;
• remuneration according to the public service contract system in Germany (TVÖD, pay group E13)
• a workspace in the beautiful city of Dresden, and a high living standard.

To apply, please submit:
(i) a complete CV including your research experience,
(ii) letter of motivation (1 page max),
(iii) transcripts of records (both bachelor and master), as well as
(iv) email addresses of at least two persons who could provide reference letters.
Please compile the above documents in this order into a single PDF file, and send it via e-mail to mgbukov@pks.mpg.de and cc markus.schmitt@ur.de, using the subject line "PhD application RL-TN: name surname". Review of applications will begin on Nov 1 (2024), and will continue until the position is filled. The starting date is flexible.

Useful references:

• F. Metz and M. Bukov, Self-correcting quantum many-body control using reinforcement learning with tensor networks, Nature Machine Intelligence 5, 780–791 (2023) (https://www.nature.com/articles/s42256-023-00687-5); see also this video (https://arxiv.org/src/2201.11790v2/anc/movie1.mp4)
• E. M. Stoudenmire and D. J. Schwab, Supervised Learning with Tensor Networks, Advances in neural information processing systems (NeurIPS) 29 (2016) (https://proceedings.neurips.cc/paper/2016/file/5314b9674c86e3f9d1ba25ef9...);
• P. Tashev, S. Petrov, F. Metz, and M. Bukov, Reinforcement Learning to Disentangle Multiqubit Quantum States from Partial Observations, arXiv:2406.07884 (https://arxiv.org/abs/2406.07884); see also this video (https://arxiv.org/src/2406.07884v1/anc/4q.Movie.mp4);
• H. N. Nguyen, F. Motzoi, M. Metcalf, K. B. Whaley, M. Bukov, and M. Schmitt, Reinforcement learning pulses for transmon qubit entangling gates, Mach.~Learn:~Sci.~Technol.~5 025066 (2024) (https://iopscience.iop.org/article/10.1088/2632-2153/ad4f4d)

The Max-Planck Society is committed to increasing the number of individuals with disabilities in its workforce and therefore encourages applications from such qualified individuals. The Max Planck Society seeks to increase the number of women in those areas where they are underrepresented and therefore explicitly encourages women to apply.