Passage de capsules via une Squeezing Particles into a Constriction: AI-Based Data Analysis micro-fluidique

Réf ABG-140543

Stage master 2 / Ingénieur

Durée 6 mois

Salaire net mensuel 650

13/10/2026

Laboratoire Biomécanique et Bioingénierie (UMR CNRS 7338)

Lieu de travail

Compiègne Les Hauts de France France

Champs scientifiques

• Sciences de l’ingénieur

Mots clés

Particles, Flow, Pores, Constriction, Machine Learning, Data Analysis

Date limite de candidature

11/11/2026

Établissement recruteur

Site web :

https://bmbi.utc.fr

Les activités de recherche du laboratoire BMBI concernent la Biomécanique et la Bioingénierie, plus particulièrement la Mécanique du Vivant et l’Ingénierie pour la Santé. La spécificité de BMBI est sa pluridisciplinarité qui se traduit par des projets de recherches interdisciplinaires associant les compétences en mécanique, physique, traitement du signal, biologie (cellulaire et moléculaire), biochimie, physiologie.

Description

Scientific context - How do soft particles squeeze through narrow pores? This question is relevant to many applications in biological flows, environmental transport, and biomedical microfluidic devices. When deformable particles flow through a constriction, their interactions with the walls and with neighboring particles can lead to complex behaviors, such as, collisions, aggregation and eventually clogging of the constriction. We study these phenomena using fully three-dimensional computer simulations of fluid-filled capsules, performed with our in-house software. By varying key parameters such as the applied flow, number of particles, particle deformability and constriction width, we generate large data-sets covering a wide range of dynamical regimes.

Project objective - The main goal of this project is to develop AI based tools for analyzing and extracting automatically information from these large simulation datasets. The student will investigate machine learning and computer vision methods to:

• automatically detect and track individual particles,
• extract particle trajectories and velocities,
• identify particle-particle and particle-wall interactions,
• classify different dynamical regimes, such as passage, and aggregation,
• identify patterns linking simulation parameters to the observed dynamics.

Required skills - This interdisciplinary project combines computational physics, fluid mechanics, data analysis, machine learning and computer vision. It offers an opportunity to work with large-scale simulation data and develop AI tools for analyzing complex physical systems. Depending on the student's interests, the developed methods can also be applied to other soft-particle systems, including existing datasets of red blood cells flowing through microfluidic channels.

Profil

• Biomécanique / biomatériaux / bioingénierie / biomédical

• Notions de mécanique des fluides et/ou solides

• Capacité à travailler en équipe dans un contexte interdisciplinaire

• Rigueur, motivation, dynamisme

Prise de fonction

01/02/2027