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Our team is engaging Physics Experts (Statistical Physics / Quantum Information / Condensed Matter) to contribute to a research-driven customer project at the intersection of theoretical physics and numerical benchmarking.
In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.
• Analyze and provide expert insights into complex statistical physics phenomena, with a focus on replicated random-bond Ising/Ashkin-Teller models, the toric-code threshold, and the Nishimori line.
• Deliver clear, well-documented solutions or critiques of problems relating to Kramers-Wannier duality, quenched disorder averaging, square-lattice self-duality, and domain-wall free energy.
• Engage in advanced numerical work, particularly around 4-state Potts model simulations and interpretation.
• Identify, discuss, and resolve technical challenges involving noncontractible loop defects and related topological features.
• Participate as a Solver, Auditor, or Adjudicator on specific project assignments based on your experience and subfield strengths.
• Advanced academic background (PhD or equivalent experience) in physics, with specialization in statistical physics, quantum information, or condensed matter theory.
• Direct, hands-on experience applying Kramers-Wannier duality, quenched disorder averaging, and square-lattice self-duality in research or project settings.
• Demonstrated proficiency in numerical simulations involving the 4-state Potts model and analysis of domain-wall free energy.
• Familiarity with topological quantum codes, particularly the toric code and its threshold phenomena.