Pierre Ronceray (CINaM)

Modeling and Measuring Soft Living Matter


2021 - present | CENTURI group leader

2019 - 2020 | Postdoctoral fellowship at the Center for the Physics of Biological Function - Princeton University

2016 - 2019 | Postdoctoral fellowship at the Center for Theoretical Science - Princeton University

2013 - 2016 | PhD in Theoretical Physics, Université Paris -Sud


Luminy campus, Marseille (France)

About his research

Understanding the organization and dynamics of living matter, from the protein level to the tissue level, is a tremendous challenge from a physicist’s point of view. My goal is to identify simple physical laws governing this complex, heterogeneous, non-linear, out-of-equilibrium state of matter. I propose a dual theoretical approach to this problem. On the one hand, I address the direct problem: through analytic calculations, computational modeling and collaborations with experimentalists, I explore the assembly, mechanics and thermodynamics of cellular structures. On the other hand, I design methods for the inverse problem: inferring the dynamical properties of these systems – such as force, diffusion and stress fields – from microscopy data. Indeed, while these quantities are central objects in physical theories of soft biological matter, they are not directly observable: to advance our understanding of biological matter, novel data analysis methods adapted to modern experimental techniques are required.

Open position

Postdoctoral fellow position 1 open position

Postdoc position on stochastic inference

A two-year postdoctoral position on data-driven approaches to biological dynamics is available in my soft biophysics theory group at the Turing Centre for Living Systems (Centuri / CINaM), in Marseille, France. The goal is to develop and apply techniques to learn stochastic dynamical equations from experimental data of biological and soft matter systems. More specifically, this postdoc will focus on improving the representation of inferred force fields through the use of, e.g., nonlinear parametric forms, neural nets or kernel methods. In parallel with method development, the postdoc will apply the techniques to experimental datasets, notably in the context of collective motion of animals. This will involve a collaboration with Ramiro Godoy-Diana and Benjamin Thiria (ESPCI, Paris) to explore their fish schooling data.


A strong theoretical background in statistical physics, statistics or machine learning is required, as well as willingness to engage with experimentalists and work with data. This position will be funded by the ERC "SuperStoc - Super-resolved stochastic inference: learning the dynamics of soft biological matter." To apply, please send a CV and a statement of motivation to pierre.ronceray@univ-amu.fr, as well as the names of two references for recommendation letters. Applications will start being reviewed on Nov 15th and until the position is filled.