Biophysics / Statistical Physics Physicist for AI Training

Apply advanced biophysics and statistical-physics expertise to a research-level project modeling bacterial population growth, stochastic two-state growth-rate switching, cell-size regulation noise, and asymptotic population growth rates. Your analysis will help train next-generation AI systems through rigorous, real-world technical input. Prior AI experience is not required.

• Analyze and model stochastic two-state Markov processes with gamma-distributed waiting times in bacterial population dynamics.
• Apply the Euler-Lotka equation to assess and benchmark asymptotic population growth rates across varying noise regimes.
• Develop or review analytical methods using perturbative expansions in small division-noise variance, and validate resulting predictions.
• Use renewal theory and first-passage-time analysis to derive insights into population growth and cell-size regulation noise.
• Document methodological steps, assumptions, and findings clearly to support AI training and reproducibility.

• 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 for population or growth dynamics.
• Demonstrated ability to conduct first-passage-time analysis and interpret implications for growth-rate fluctuations and size control.

• Remote independent contractor engagement.
• Depending on subfield specialization, seniority, and hands-on experience with the relevant methods, participants may be considered for Solver, Auditor, or Adjudicator assignments.

$80 to $160 per hour.