micro1 is engaging Physics Experts (Biophysics / Statistical Physics) to contribute to a research-level project focused on modeling bacterial population growth, stochastic two-state growth-rate switching, cell-size regulation noise, and asymptotic population growth rate.
In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input.
No prior experience in AI is required — your domain knowledge is what matters. Participants may be considered for one or more assignments, including Solver, Auditor, or Adjudicator, based on subfield specialization, seniority, and hands-on experience with the relevant methods.
Responsibilities
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 resulting predictions.
Integrate renewal theory and first-passage-time analysis to derive key insights on population growth and cell-size regulation noise.
Document detailed methodological steps, assumptions, and findings clearly for AI training and reproducibility purposes.
Preferred skills
Advanced expertise in statistical physics, biophysics, or related 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, and renewal theory in the context of population or growth dynamics.
Demonstrated ability to conduct first-passage-time analysis and interpret implications for growth-rate fluctuations and size control.
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Hiring companyMicro1
