PhD student (M/F) in Physics of Complex and Living Matter

Job Description

Thesis Subject

PhD Project Description
Title: Inference of Stochastic Dynamics in Developing Active Biological Systems

Living tissues, migrating cells, or active particles constitute out-of-equilibrium systems where internal forces, collective interactions, and the geometry of the environment shape complex dynamics. Their quantitative description requires methods capable of extracting, from noisy and partially observed trajectories, the effective forces, interactions, laws of motion, as well as the influence of hidden variables or non-white noise.

Within this project, the thesis will aim to develop and apply a new generation of stochastic dynamics inference methods, at the crossroads of statistical physics, biophysics, and machine learning. These approaches will build on tools recently introduced in the group, in particular:
• nonlinear methods of parametric and non-parametric inference;
• latent variable augmentation coupled with EM schemes to improve identifiability;
• the extension of the formalism to dynamical systems subject to colored noise (memory, subdiffusivity, temporal correlations), particularly relevant for active biological systems.

Indicative Scientific Plan
4. Methodological development
◦ Extension of nonlinear inference methods (parametric and non-parametric).
◦ Integration of an EM scheme with variable augmentation to handle hidden variables and identifiability biases.
5. Validation on model systems
◦ Controlled simulations (nonlinear SDEs, active models, dynamics with memory).
◦ Study of the robustness of the methods (noise, low sampling frequency, partial observability).
6. Application to biological data
◦ Analysis of trajectories in developing tissues (e.g. Drosophila embryo, ciliated epithelium, multicellular systems).
◦ Reconstruction of effective interactions, force fields, and active contributions.
◦ Biophysical interpretation in collaboration with experimental partners.

Your Work Environment

The PhD student will join an active group in theoretical biophysics and computational inference (SuperStoc group, ERC), with strong ties to experimental collaborations within the Turing Centre for Living Systems (Centuri, Aix-Marseille Université). The thesis will combine theory, analysis of real data, and interaction with biological applications. The project offers a framework conducive to original contributions, both conceptual and methodological.

The working environment is international and multidisciplinary. The PhD student will benefit from regular supervision, access to Centuri computing resources, and will participate in national and international summer schools and conferences.

Constraints and risks

Screen-based work. No physical constraints or particular risks.

Compensation and benefits

Compensation

2300 € gross monthly

Annual leave and RTT

44 jours

Remote Working practice and compensation

Pratique et indemnisation du TT

Transport

Prise en charge à 75% du coût et forfait mobilité durable jusqu’à 300€

About the offer

About the CNRS

The CNRS is a major player in fundamental research on a global scale. The CNRS is the only French organization active in all scientific fields. Its unique position as a multi-specialist allows it to bring together different disciplines to address the most important challenges of the contemporary world, in connection with the actors of change.

CNRS

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