$100–200/hr · micro1
PhD physicist specializing in statistical physics or quantum information who critiques and solves advanced theoretical problems for AI training.
What you would do
- Solve complex problems involving replica symmetry breaking, topological codes, and lattice models
- Run and validate numerical simulations, particularly 4-state Potts model and toric-code thresholds
- Critique AI-generated physics solutions for theoretical consistency and numerical correctness
- Assess AI understanding of concepts like Kramers-Wannier duality and quenched disorder averaging
- Evaluate handling of subtleties in topological defects and domain-wall free energy
Who they want
- PhD in physics with emphasis on quantum information, statistical mechanics, or condensed matter physics
- Hands-on experience applying Kramers-Wannier duality and quenched disorder averaging in research or project work
- Demonstrated proficiency running and interpreting Potts model and toric code numerical simulations
- Familiarity with topological quantum codes and threshold phenomena in quantum error correction
- 5-10+ flexible hours per week available for remote, asynchronous work
Main skills
What the interview asks about
1.Numerical correctness and scaling
Numerical simulations are only trustworthy if error bars, finite-size effects, and convergence are handled properly; interviewers test whether you spot when AI outputs violate these principles.
For example: “AI reports Potts critical temperature from 100x100 lattice with 1,000 MC steps. Finite-size effects should dominate here. What red flags indicate the result is unreliable?”
2.Replica trick subtleties
Replica methods involve counterintuitive mathematics; interviewers test whether you catch errors in replica-limit taking and replica-symmetry-breaking assumptions.
For example: “AI claims taking n→0 limit after computing partition function is valid because replicas are fictitious. Explain why naive replica symmetry breaking fails here and when it leads to wrong answers.”
3.Topological reasoning and defects
Topological arguments require careful thinking; missing a defect type or misunderstanding boundary conditions leads to wrong physics, so interviewers test your precision.
For example: “Toric code on torus: AI claims non-contractible loops determine topological sector. Describe defects' role in ground-state degeneracy and verify if the statement is complete.”
4.Disorder averaging under constraints
Quenched disorder is counterintuitive; averaging over disorder while respecting constraints (like the Nishimori line) requires care, so interviewers test whether you spot when AI skips important steps.
For example: “Random-bond Ising: AI averages disorder without Nishimori constraint. Why does this constraint matter? Give an example where ignoring it gives wrong conclusions.”
A task you may get
Critique an AI analysis of Kramers-Wannier duality on a disordered lattice, including numerical results. Identify conceptual errors, numerical issues, and missing subtleties.
How to prepare
- Review your PhD thesis or key papers on your specialization; identify the hardest subtleties and places you've seen reasoning go wrong.
- Run a small Potts or Ising model simulation yourself; understand finite-size effects and how confidence in the critical temperature grows with system size.
- Study a recent review on topological quantum codes, especially threshold phenomena and the role of defects.
- Prepare a 5-minute explanation of one subtle concept in your subdomain that even physicists often get wrong.
The facts
- Pay
- $100–200/hr
- Open to
- Bangladesh, Hong Kong, India, Indonesia, Japan, Kazakhstan, Kyrgyzstan, Malaysia, Pakistan, Philippines, Singapore, Sri Lanka, Taiwan, Thailand, Uzbekistan, Vietnam, Austria, Belarus, Belgium, Denmark, France, Germany, Greece, Italy, Netherlands, Portugal, Russia, Spain, Switzerland, United Kingdom, Argentina, Brazil, Chile, Colombia, Mexico, Peru, Algeria, Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Palestine, Qatar, Saudi Arabia, Tunisia, United Arab Emirates, United States, Canada, Nigeria, Kenya, South Africa, Ghana, Ethiopia
- Field
- Sciences Research
- Role type
- Expert
- Posted
- 9/7/2026
- Places left
- 10
We wrote this page from the public micro1 listing. It may be out of date, so read the full posting before you apply.