Stochastic Biophysics Physicist (PhD)

## About the work

**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.

## Research areas in this panel

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.

## Methods we expect to find in your own publications

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

• Chemical master equation and the stochastic Hinshelwood cycle
• Multi-type branching processes and circulant matrix spectra
• Moment hierarchies for reaction networks
• Generating-function methods

## Who we are looking for

**A PhD in biophysics, statistical physics, applied mathematics or a closely related field.** This is a hard requirement. Postdoctoral researchers, research scientists and junior faculty are the strongest fit. Senior PhD students with a strong first-author record are welcome to apply.

**Published work on stochastic growth or reaction-network dynamics specifically.** This is the single most common reason we decline otherwise excellent researchers. Command of the methods is not enough if you have not published on the phenomenon itself.

**A verifiable publication record.** Three to five representative papers with arXiv IDs or DOIs, ideally from the last five years. First author strongly preferred. Every paper you list will be checked against the public record.

**Working proficiency with LaTeX, Python, SymPy and Jupyter.** Some familiarity with an agentic coding extension in VS Code is useful. Gaps there are acceptable if you declare them honestly.

**English at B2 or above**, including written reasoning. A large part of the value you add is how clearly you set out your argument.

## Application steps

• **Apply and complete the attached form.** Basic information, education, research experience, your method self-attestation, and the area above if you are a fit. Give an arXiv ID or DOI of **your own paper** as proof, with your author position and the methods it demonstrates. A selection without proof is not scored.
• **We verify your papers and authorship** against the public record.
• **Then one of two things happens.** Either we onboard you directly, or we invite you to a short live alignment call to agree the area and the assignment with you.
• A brief **30 to 45 minute assessment** may be added, but only where we need it. Most applicants will not see one.

## Commitment and rate

**10 hours per week**, sustained across an **8 to 10 week** window, starting immediately. Remote and asynchronous with no fixed hours.

**$80 to $1