PhD student (M/F) in the physics of complex and living matter

Job Description

Thesis Subject

Title: Stochastic Dynamics of Living and Active Matter: Theory, Inference, and Applications

Biological systems and active matter — living tissues, cells, self-propelled particles, micro-organisms, molecular assemblies — are out-of-equilibrium systems whose dynamics emerge from the coupling between stochastic fluctuations, internal forces, collective interactions, and geometry. Their quantitative understanding requires both new theoretical developments in nonequilibrium statistical physics and methods able to extract, from noisy and partially observed experimental data, the effective laws that govern them.

Depending on the candidate's profile and interests, the thesis may develop along one or several of the following directions, at the crossroads of statistical physics, biophysics, and machine learning:
• Development of stochastic-dynamics inference methods: parametric and non-parametric approaches, latent-variable augmentation, treatment of colored noise (memory, subdiffusivity, temporal correlations).
• Theory of active matter and out-of-equilibrium stochastic processes: stochastic thermodynamics, entropy production, collective dynamics.
• Machine-learning approaches for the physics of living systems.
• Application to real experimental data: developing tissues, cell migration, microbial dynamics, animal behavior, multicellular systems.

Indicative Scientific Plan
4. Methodological development
◦ Theoretical and methodological developments along the chosen direction (inference, active-matter theory, or machine learning).
◦ Algorithmic implementation and validation of the developed tools.
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 real systems
◦ Analysis of data from living or active systems (developing tissues, cell migration, microbial dynamics, animal behavior).
◦ Reconstruction of effective interactions, force fields, and active contributions.
◦ 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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