Remote PhD Physics Expert for AI-Ready Solutions

Cephas Consultancy Services Private Limited; California (MO); Hybrid; USD 83,000 - 193,000; Full time

Job summary

Cephas Consultancy Services Private Limited in the United States is seeking a Physics Expert (PhD / Postdoc) on a contractor basis for a remote engagement. You will apply deep physics knowledge to train next-generation AI systems and contribute high-impact scientific input.

The work requires rigorous derivations in LaTeX, precise solutions, and clear documentation. You will use SymPy, Python, and Jupyter for verification, identify subtleties, and iterate on feedback over an 8-10 week period at

Qualifications

• PhD in physics or active PhD candidacy.
• Research in listed subfields or closely related areas.
• 2-5 recent publications with arXiv or DOI records.
• Proficiency with LaTeX, SymPy, Python, and Jupyter.
• Strong written technical communication.
• Availability for 8-10 weeks at ~10 hours/week.

Responsibilities

• Solve advanced physics problems with rigorous derivations.
• Provide precise solutions with clear assumptions and results in LaTeX.
• Use SymPy, Python, and Jupyter for verification and workflows.
• Identify subtleties, boundary conditions, and dimensional consistency.
• Flag ambiguities and propose interpretations as needed.
• Iterate on solutions based on reviewer feedback.
• Maintain reproducible documentation standards.

Skills

Analytical thinking

Technical communication

Problem solving

LaTeX proficiency

Education

PhD in Physics

Tools

LaTeX

SymPy

Python

Jupyter

Job description

Cephas Consultancy Services Private Limited in the United States is seeking a Physics Expert (PhD / Postdoc) on a contractor basis for a remote engagement. You will apply deep physics knowledge to train next-generation AI systems and contribute high-impact scientific input.

The work requires rigorous derivations in LaTeX, precise solutions, and clear documentation. You will use SymPy, Python, and Jupyter for verification, identify subtleties, and iterate on feedback over an 8-10 week period at