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# Physicist for AI Model Training (Statistical Physics / Quantum Information

- **Company:** George Washington
- **Location:** Washington, DC, United States (Remote available)
- **Salary:** $166,400.0 - $332,800.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Machine Learning
- **Published:** August 4, 2026
- **Apply:** https://www.careerjet.com/job/register/us428cad3d786b09416b503fe7a600c8a1

## About the Role

* Advanced academic background, PhD or equivalent experience, in physics with specialization in statistical physics, quantum information, or condensed matter theory preferred.
 * Direct, hands-on experience applying Kramers-Wannier duality, quenched disorder averaging, and square-lattice self-duality in research or project work.
 * Demonstrated proficiency with numerical simulations and analysis of the 4-state Potts model and domain-wall free energy.
 * Familiarity with topological quantum codes, particularly the toric code and its threshold phenomena, and experience addressing noncontractible loop defects.

Work Terms

 * Engagement type: Independent contractor.
 * Location: Remote work.
 * Assignments are project-driven, candidates will be matched to specific tasks based on expertise, and may serve as Solver, Auditor, or Adjudicator for different assignments.

## Description

* Analyze and provide expert commentary on replicated random-bond Ising and Ashkin-Teller models, the toric-code threshold, and the Nishimori line.
 * Deliver precise, documented solutions or critiques for problems involving Kramers-Wannier duality, quenched disorder averaging, square-lattice self-duality, and domain-wall free energy.
 * Perform or interpret advanced numerical simulations, with emphasis on 4-state Potts model numerics and domain-wall free energy analysis.
 * Identify, discuss, and resolve technical challenges related to noncontractible loop defects and other topological features.
 * Contribute as a Solver, Auditor, or Adjudicator on assigned tasks depending on your experience and subfield strengths, producing high-quality inputs used to train and evaluate AI models., * Express interest through the hiring platform. Candidates will be identified and vetted by a recruitment process that includes skills review.
 * Successful candidates will be invited to participate in project assignments and matched to roles according to their experience and subfield strengths.

Eligibility

 * No prior AI experience is required, domain expertise in the listed physics areas is the primary requirement.
 * Candidates must be able to contract as independent contractors in their jurisdiction and perform remote work.


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