
Physics Expert (PhD / Postdoc) | Remote Worldwide
Listing checked September 8, 2026 · pay as published by Micro1
Overview
micro1 is looking for physics researchers at the PhD or postdoc level to contribute their expertise to a high-impact project focused on training next-generation AI systems. You will solve advanced problems from your specialization, document your reasoning in LaTeX, and verify results using SymPy, Python, and Jupyter. No prior AI experience is needed; your domain knowledge is the core qualification. This remote contractor role offers a flexible schedule of 10-15+ hours per week, with the option to work after hours or on weekends.
What You'll Do7
- 1Tackle advanced physics problems within your area of specialization, producing rigorous derivations and analyses.
- 2Write clear, technically precise solutions that spell out assumptions, approximations, and final results using LaTeX notation.
- 3Use SymPy, Python, and Jupyter to run symbolic or numerical checks and build transparent computational workflows.
- 4Spot and explain subtleties in problem statements, such as special cases, boundary conditions, and dimensional consistency.
- 5Flag ambiguities in project materials and suggest reasonable interpretations or clarifications.
- 6Revise your solutions based on reviewer feedback, making sure corrections are integrated smoothly.
- 7Keep documentation and reproducibility at a level consistent with professional research standards.
Requirements7
- 1PhD in physics or advanced-stage PhD candidacy with active research experience in a relevant subfield.
- 2Research expertise in one or more areas: High Energy Physics, Mathematical Physics, Biophysics, Statistical Physics, Condensed Matter (including moiré systems, magnetism, PXP/Rydberg), AMO/Quantum Optics, Gravitation, Cosmology, Astrophysics, Quantum Information, or Optical Properties of Materials.
- 32–5 recent representative publications (within the past ~5 years) with accessible arXiv or DOI records.
- 4Proficiency with LaTeX for mathematical presentation and with SymPy, Python, and Jupyter for computational tasks; willingness to indicate areas where you might need support.
- 5Strong written technical communication skills, with a history of producing clear, precise, and well-argued scientific outputs.
- 6Analytical strength to isolate key physical principles and deliver nuanced solutions to complex problems.
- 7Availability to engage consistently over an 8–10 week period, roughly 10 hours per week.
Who Should Apply
This role suits a physics researcher who enjoys the challenge of solving well-defined problems and explaining their reasoning in writing. If you have a PhD or are close to finishing one, with publications in a listed subfield, and you are comfortable with LaTeX and Python-based tools, you will likely thrive here. The role is less suitable for someone who prefers hands-on lab work or who is not interested in documentation-heavy tasks. Candidates often get rejected when their publication record is thin or not recent, or when their written solutions lack clarity and rigor. Also, if you cannot commit to the 8–10 week engagement, this may not be the right fit.
Salary Insight
The pay rate is $100–$200 per hour, depending on experience and qualifications. The exact rate is not specified in the posting, so it will likely be discussed during the hiring process.
Pay and demand for Machine Learning & AI roles
AggregatedTypical pay
$70/hour
This role
$100–$200/hr
Most Machine Learning & AI roles pay $50–$100 per hour. This role's pay sits above that range.
Based on 605 similar roles that publish pay · 102 publish only a top rate; those count at the rate they gave
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Most requested skills · share of roles
- python14%
- llm evaluation8%
- ai training6%
- quality assurance6%
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Compensation
$100–200/hr
Required Skills
Application Tip
When applying, highlight your most relevant publications and include links to arXiv or DOI records. Also, mention specific examples where you used SymPy or Python to verify a complex derivation, as this shows you can handle the computational side of the work.
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