Physics PhD for AI Training, Statistical Physics / Quantum Information / Cond...

Apply advanced theoretical physics expertise to help train and evaluate next-generation AI systems through research-driven work in statistical physics, quantum information, and condensed matter theory. This role centers on rigorous problem solving, numerical benchmarking, and expert feedback for complex physics topics.

• Analyze replicated random-bond Ising and Ashkin-Teller models, toric-code thresholds, and the Nishimori line.
• Develop clear, well-documented solutions and critiques involving Kramers-Wannier duality, quenched disorder averaging, square-lattice self-duality, and domain-wall free energy.
• Perform and interpret advanced numerical work involving 4-state Potts model simulations.
• Identify and resolve technical questions involving noncontractible loop defects and related topological features.
• Contribute to assignments as a Solver, Auditor, or Adjudicator based on relevant experience and subfield expertise.

• PhD or equivalent advanced experience in physics, specializing in statistical physics, quantum information, or condensed matter theory.
• Hands-on research or project experience with Kramers-Wannier duality, quenched disorder averaging, and square-lattice self-duality.
• Proficiency with 4-state Potts model numerical simulations and domain-wall free-energy analysis.
• Familiarity with topological quantum codes, particularly the toric code and threshold phenomena.
• No prior AI experience is required.

• Part-time independent contractor engagement.
• Fully remote and open to candidates worldwide.
• Flexible schedule of 5 to 10 or more hours per week, including after-hours and weekend availability if needed.

$100 to $200 per hour.