Biophysics and Statistical Physics Expert for AI Model Training

Apply advanced statistical physics and biophysics methods to model bacterial population growth with stochastic two-state growth-rate switching, cell-size regulation noise, and asymptotic population growth rate. Your analytical work will be used to produce high-quality, reproducible outputs that train and evaluate next-generation AI systems. No prior AI experience is required; domain expertise is the primary qualification.

• Analyze and model stochastic two-state Markov processes with gamma-distributed waiting times in the context of bacterial population dynamics.
• Apply the Euler-Lotka equation to assess and benchmark asymptotic population growth rates under varying noise regimes.
• Develop or review analytical approaches using perturbative expansions in small division-noise variance and validate the resulting predictions.
• Integrate renewal theory and first-passage-time analysis to derive insights on population growth and cell-size regulation noise.
• Document methodological steps, assumptions, derivations, and findings clearly to support reproducibility and use in AI training datasets.
• Contribute as one or more of the following roles based on expertise: Solver, Auditor, or Adjudicator.

• Advanced expertise in statistical physics, biophysics, or quantitative biological modeling of stochastic processes.
• Proven experience working with two-state Markov models and gamma-distributed waiting times in biological or physical systems.
• Strong command of the Euler-Lotka equation, perturbative expansions in small division-noise variance, and renewal theory applied to population or growth dynamics.
• Demonstrated ability to conduct first-passage-time analysis and interpret implications for growth-rate fluctuations and size control.
• No prior AI experience required; assignments and seniority will be determined by subject-matter depth and hands-on experience.

• Engagement type: Independent contractor, remote.
• Participants may be considered for one or more assignments, including Solver, Auditor, or Adjudicator, depending on subfield specialization, seniority, and practical experience with the listed methods.
• Deliverables include analytical derivations, validated predictions, and clear methodological documentation suitable for AI training and reproducibility.

• Pay range: $80.00 to $160.00 per hour, paid to contractors.

• No requirement for prior AI or machine learning experience; selection is based on technical expertise in the listed topics.
• Must be able to work as an independent contractor in a remote capacity.

Submit a brief application including your CV, a summary of relevant modeling work, and representative examples or publications that demonstrate experience with two-state Markov processes, gamma-distributed waiting times, Euler-Lotka analyses, perturbative expansions, renewal theory, or first-passage-time calculations. Applications will be reviewed through an AI-enabled screening and technical vetting process; successful candidates will be invited to one or more assignments based on fit and demonstrated expertise.