Postdoctoral Associate in Theoretical and computational quantum information

A Postdoctoral Associate position in theoretical and computational quantum information science and condensed matter physics is available in the group led by Prof. Haining Pan in the Department of Physics at the University of Florida, starting Fall 2027.

The initial appointment is for two years, renewed annually, and may be extended for a third year contingent upon strong performance, mutual satisfaction, and the availability of funds.

The successful candidate will conduct numerical and theoretical research on quantum computing, AI-accelerated quantum simulation and materials discovery, quantum machine learning, topological quantum materials, and/or related areas.

The successful candidate will have access to Hi Per Gator , the fastest university-owned supercomputer in the United States (TOP
500), including 504 NVIDIA Blackwell B200 GPUs. These resources are provisioned directly through the group's institutional allocation.

The University of Florida is the state's flagship land-grant, Carnegie R1, and AAU research university, ranked among the top 10 U.S. public universities (U.S. News & World Report, 2027). The Department of Physics hosts a large condensed matter and quantum information community of roughly twenty-five faculty across theory and experiment. The department operates the High B/T Facility of the National High Magnetic Field Laboratory through its Micro kelvin Laboratory.

Faculty also participate in the Center for Condensed Matter Sciences and the Quantum Theory Project, among the world's largest centers for the theory and computation of complex molecular and materials systems. Gainesville offers a substantially lower cost of living than most major research hubs and lies within about two hours of Orlando, Jacksonville, and Tampa;
Florida levies no state income tax.

The position will be open until filled with application review beginning on December 15, 2026.

Ph.D. in Physics, Computer Science or closely related areas

Demonstrated strength in one or more of the following.

• Quantum many-body numerics: tensor-network methods, variational Monte Carlo (including neural quantum states), stabilizer and near-Clifford simulation, Gaussian/free-fermion methods, open-system dynamics (Lindblad master equations).
• Machine learning for physical systems: deep learning, reinforcement learning, transformer architectures and large language models.
• Analytical many-body and quantum information theory: replica field theory approaches to monitored and noisy quantum circuits, symmetry-protected topological phases, topological quantum computation, quantum error correction.