Stochastic Biophysics Physicist (PhD)

CritPt is a public benchmark of research-level physics challenges, built to test whether frontier AI models can carry out genuine physics research reasoning rather than textbook problem solving. The benchmark paper is [arXiv:2509.26574](https://arxiv.org/abs/2509.26574) and we recommend reading it before applying. It will tell you quickly whether this work interests you.

We are engaging physicists to work on research-level physics problems in their own subfield. Depending on where your publication record fits, that can mean creating problems, solving them, reviewing completed work, or auditing it. We agree the specific assignment with you once you are matched to an area.

This is research-grade work rather than volume work. Whatever you produce has to be complete enough for another specialist in your subfield to follow and verify independently, so written reasoning is part of every assignment.

One area. We match narrowly: you need to have published on this specific phenomenon, not in biophysics broadly.

1. Stochastic autocatalytic growth: chemical master equation, branching processes: Autocatalytic reaction cycles, stochastic Hinshelwood cycle, chemical master equation processes, circulant matrix spectra, transient oscillations toward balanced exponential growth, moment hierarchies for reaction networks, generating-function methods.

If you also work on long-range dispersal, heavy-tailed jump processes or front propagation in range expansions, say so on the form. A closely related area sits in our statistical physics panel and draws on the same methods.

You should be able to point to your own papers demonstrating at least one of the following families: