Remote Biophysics & Statistical Physics Contractor

micro1; United States; Remote; USD 124,000 - 220,000; Part time

Job summary

micro1 is seeking a Physics Expert (Biophysics / Statistical Physics) on a remote, contractor basis to contribute to a research-level project examining bacterial population growth, noise in division, and cell-size regulation. You will shape AI training data by providing high-quality domain insights and modeling expertise without requiring prior AI experience.

The role emphasizes rigorous stochastic modeling, renewal theory, and first-passage-time analysis to derive actionable predictions for

Qualifications

• Advanced expertise in statistical physics, biophysics, or related quantitative biological modeling of stochastic processes.
• Proven experience with two-state Markov models and gamma-distributed waiting times in biological or physical systems.
• Strong command of the Euler-Lotka equation, perturbative expansions, and renewal theory in growth dynamics.

Responsibilities

• Analyze and model stochastic two-state Markov processes with gamma-distributed waiting times in bacterial population dynamics.
• Apply Euler-Lotka equation to assess asymptotic population growth rates under varying noise.
• Develop analytical approaches using perturbative expansions in small division-noise variance and validate predictions.

Skills

Two-state Markov models

Gamma-distributed waiting times

Euler-Lotka equation

Perturbative expansions

Renewal theory

First-passage-time analysis

Stochastic processes

Biophysics

Job description

micro1 is seeking a Physics Expert (Biophysics / Statistical Physics) on a remote, contractor basis to contribute to a research-level project examining bacterial population growth, noise in division, and cell-size regulation. You will shape AI training data by providing high-quality domain insights and modeling expertise without requiring prior AI experience.

The role emphasizes rigorous stochastic modeling, renewal theory, and first-passage-time analysis to derive actionable predictions for