Bacterial Population Physics Modeler

The Work

You will support AI training based on stochastic bacterial population physics. The work combines quantitative modeling, analytical research review, and clear documentation so AI systems can learn from expert reasoning.

• Analyze two-state Markov processes with gamma-distributed waiting times for bacterial population models.
• Use the Euler-Lotka equation to assess and benchmark long-term population growth rates under different noise conditions.
• Develop or review perturbative expansions for small division-noise variance and validate the resulting predictions.
• Apply renewal theory and first-passage-time analysis to study population growth and noise in cell-size regulation.
• Document methods, assumptions, and findings clearly for AI training and reproducibility.
• Provide careful written feedback and follow detailed project instructions.

What It Pays and Takes

This is a part-time, remote contractor role for an experienced quantitative researcher. Prior AI training experience is not required, but strong subject knowledge is essential.

• Pay: $80 to $160 per hour.
• Hours: Less than 20 hours per week.
• Work arrangement: Remote, part-time contract work.
• Language: English.
• Location: Applicants must be based in one of the eligible countries listed in the application.
• Education or expertise: Advanced knowledge of statistical physics, biophysics, or related quantitative biological modeling of stochastic processes.
• Technical background: Experience with two-state Markov models, gamma-distributed waiting times, the Euler-Lotka equation, perturbative expansions, and renewal theory.
• Analysis skills: Ability to conduct first-passage-time analysis and interpret growth-rate fluctuations and size-control effects.
• Work style: Strong written communication, close attention to detail, independence, and comfort working remotely.

How It Works

Apply on OpenTrain with your resume, then complete the application on the hiring site.

About AI Training Work

AI training is the human work behind systems that learn from examples, including reviewing technical material and rating model responses. People with deep subject expertise are valuable because they can judge complex answers, explain their reasoning, and help make AI outputs more accurate.