Postdoctoral Associate in Theoretical and computational quantum information

University of Florida

Gainesville (FL)

On-site

USD 52,000 - 68,000

Full time

3 days ago
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Benefits offered by this job

HiPerGator access

Job summary

University of Florida invites applications for a Postdoctoral Associate in theoretical and computational quantum information science and condensed matter physics, led by Prof. Haining Pan.

The appointment starts Fall 2027 and is initially two years, with annual renewals based on performance and funds. The successful candidate will study quantum computing, AI-accelerated quantum simulation, materials discovery, quantum machine learning, and topological quantum materials.

Qualifications

  • Ph.D. in Physics, Computer Science or closely related areas.
  • Demonstrated strength in quantum many-body numerics, ML for physical systems, or analytical many-body theory.

Responsibilities

  • Conduct numerical and theoretical research on quantum computing and quantum information science.
  • Explore AI-accelerated quantum simulation and materials discovery.
  • Investigate quantum machine learning and topological quantum materials.

Skills

Tensor-network methods
Variational Monte Carlo
Machine learning for physics
Quantum information theory
Topological quantum materials

Education

Ph.D. in Physics or Computer Science

Tools

HPC systems
Tensor-network software

Job description

Position Description

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 HiPerGator, the fastest university-owned supercomputer in the United States (TOP500), 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 Microkelvin 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.

Required Qualifications

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

Preferred Qualifications

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.
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