Missions

As part of ongoing research at CINaM on stochastic dynamics in living systems, the postdoctoral researcher will carry out a theoretical study in statistical physics and inference to analyze the fundamental limits of force field learning from imperfect data. In particular, the goal will be to extend previous theoretical work on learnability from perfect trajectories to more realistic cases involving noisy and/or partial data, with special attention to information-theoretic constraints.

Activités

Rigorous mathematical reformulation of existing results on learnability from perfect data.
• Theoretical extensions to handle the case of incomplete and/or noisy trajectories.
• Development of a coherent framework to quantify the “learnability” of a hidden random variable from observable data.
• Design of algorithmic approaches in regimes where force field learning is impossible, to infer properties of the effective (e.g. colored) noise.
• Interaction with team members working at the interface of theory and biophysics.

Compétences

Strong background in statistical and/or theoretical physics.
• Proficiency in scientific computing tools, in particular Python programming.
• Basic knowledge of machine learning and stochastic data analysis.
• Ability to work independently, initiative, and interest in fundamental questions.
• Good written and oral communication skills in English.

Contexte de travail

The work is part of a fundamental research project conducted at CINaM, within a dynamic team at the interface of statistical physics, inference, and biophysics. The position is fully theoretical, involving daily computer-based work in a stimulating and collaborative environment. Interactions with external collaborators may take place depending on the project's needs.

Le poste se situe dans un secteur relevant de la protection du potentiel scientifique et technique (PPST), et nécessite donc, conformément à la réglementation, que votre arrivée soit autorisée par l'autorité compétente du MESR.

Contraintes et risques

Prolonged sedentary computer work.
• Mostly autonomous work requiring strong time and priority management.
• Significant conceptual challenges requiring perseverance and creativity.
• No experimental handling or associated physical risks.

Informations complémentaires

The unit is located in a sensitive “Restrictive Zone Regime” sector due to its research themes.