Physics Research Expert (Solver)

Micro1; India; Remote; INR 3,234,000 - 7,761,000; Part time

Job summary

Cashback24x7 seeks a Physics Research Expert (Solver) for a remote, contractor/part-time role (10 hrs/week). You will apply domain knowledge to train AI systems, solving complex physics problems and providing rigorous, publication-ready explanations.

Requirements include a PhD in physics (or senior PhD student), 2–5 publications with arXiv/DOI, and proficiency in LaTeX, Python, SymPy, and Jupyter. Candidates must be based in the US, UK, or Canada.

Qualifications

• PhD (or senior PhD student) in physics with active ongoing research in targeted subfields.
• 2–5 representative publications (last 5 years) with arXiv/DOI links.
• Expertise in mathematical modeling, derivations, and proofs.
• Proficient in LaTeX, Python, SymPy, and Jupyter.
• Excellent written and verbal communication; independent work capability.
• Must be based in the US, UK, or Canada (rare exceptions possible).

Responsibilities

• Contribute deep domain expertise for AI training initiatives with authentic insights.
• Formulate, solve, and document advanced mathematical physics problems.
• Develop and validate computational models, simulations, and analytic solutions using Python, SymPy, and Jupyter.
• Produce high-quality, publication-level explanations and derivations with clear reasoning.
• Communicate concepts clearly with project coordinators and the AI team.
• Work independently to tackle novel research problems within the subfield.
• Review and critique solutions to improve accuracy for AI training.

Skills

Physics domain expertise

Mathematical modeling

Publications (2–5 in last 5 years)

Strong written and verbal comms

Independent researcher

Education

PhD in physics

Senior PhD student

Tools

Python

SymPy

Jupyter

LaTeX

Job description

Job Title: Physics Research Expert (Solver)

Job Type: Contractor/ Part-Time (10 hrs. a week)

Location: Remote

Job Summary: 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.

Key Responsibilities:

• Contribute deep domain expertise in your specialized physics subfield, providing authentic, up-to-date insights for AI training initiatives.
• Formulate, solve, and document advanced mathematical physics problems using rigorous methodologies.
• Develop and validate computational models, simulations, and analytical solutions leveraging Python, SymPy, and Jupyter environments.
• Produce high-quality, publication-level written explanations and derivations with clear, structured reasoning.
• Clearly communicate complex concepts and solutions through both written and verbal exchanges with project coordinators and the technical AI team.
• Work independently to navigate and resolve challenging, novel research problems aligned with the assigned subfield.
• Leverage your experience to review, critique, and improve problem solutions, ensuring accuracy and clarity for AI training.

Required Skills and Qualifications:

• PhD (or senior PhD student) in physics with active, ongoing research in one or more targeted subfields: High Energy/Mathematical Physics, Biophysics/Statistical Physics, Condensed Matter, AMO/Quantum Optics, Gravitation/Cosmology/Astrophysics, Quantum Information, or Optical properties of materials.
• 2–5 representative publications (last 5 years) in the relevant subfield, with available arXiv/DOI links.
• Expertise in mathematical modeling, derivations, and system proofs at a publication-worthy standard.
• Proficient in LaTeX, Python, SymPy, and Jupyter (flag any skill gaps).
• Exceptional written and verbal communication skills—clarity, accuracy, and the ability to explain intricate concepts are essential.
• Demonstrated ability to work independently and execute research tasks with minimal supervision.
• Must be based in the US, UK, or Canada (rare exceptions for truly exceptional candidates).

Preferred Qualifications:

• Experience in AI training or interdisciplinary computational research projects.
• Recognition as a thought leader, contributor, or reviewer in your physics subfield.