Drop impact on a pool of immiscible liquid Context: The impact of a liquid drop onto a liquid surface is a commonly observed phenomenon in nature and throughout daily life, and is important for many industrial processes such as spray painting, inkjet printing and spreading of pesticides. It has been demonstrated recently that this process could be used for the mass production of particles with complex shapes and cell encapsulation [1]. The geometry of the resulting particles strongly depends on the impact dynamics and the deformation of the interfaces. Goals: In this project, we propose to investigate the formation of complex encapsulations formed by the impact of a liquid drop on a pool of immiscible liquid. We will systematically study the impact of water drops on a pool of an immiscible liquid such as silicone oil. We will combine high-speed imaging experiments with high resolution numerical simulations (using the open-source code Basilisk) to investigate these complex dynamics, and uncover the physical processes involved. Profile: Candidates should have a good training in Fluid Mechanics. The project can either be focused on experimental observations and/or numerical simulations depending on the applicant. Environment: The project will take place in the laboratory of Prof. Marie-Jean THORAVAL at LadHyX in École Polytechnique, in the South of Paris.

Impact of compound drops: Bouncing or Sticky? - Context: The impact of a drop onto a solid or liquid surface has a wide range of applications including combustion, 3D printing, biological microarrays, pharmaceutics and the food industry. While most of them rely on single fluid drops, the emergence of new additive manufacturing techniques promises to revolutionize these industries by combining multiple fluids into compound drops. One of the critical challenges in these applications is to control the deposition process of the impacting drop and therefore its spreading, potential rebound and splashing. Goals: We propose in this project to control the rebound of the water core by varying the viscosity and thickness of the outer oil layer. We will combine high-speed imaging experiments with high resolution numerical simulations (using the open-source code Basilisk) to investigate these complex dynamics, and uncover the physical processes involved in the deposition of compound drops. Profile: Candidates should have a good training in Fluid Mechanics. The project can either be focused on experimental observations and/or numerical simulations depending on the applicant. Environment: The project will take place in the laboratory of Professor Marie-Jean THORAVAL at LadHyX in École Polytechnique, in the South of Paris.

Air film dynamics - Context: Gas transfer at the ocean surface has a critical importance for climate, as it captures around 30% of the CO2 released into the atmosphere, and for marine biological activity, as it provides the necessary O2. This transfer can be promoted by the entrapment of bubbles, produced through impacting rain drops or breaking waves. The shape and dynamics of the bubbles are important to model these transfers. Goals: We propose in this project to study the contraction dynamics of an air film into a fluid. We will systematically vary the gas and fluid properties in different geometries to understand their contraction velocity and rupture mechanisms. This project will combine numerical simulations (using the open-source code Basilisk) with theoretical analysis to uncover the physical processes involved in the gas transfer into the ocean. Profile: Candidates should have a good training in Fluid Mechanics and Computational Fluid Dynamics. Environment: The project will take place in the laboratory of Prof. Marie-Jean THORAVAL at LadHyX in École Polytechnique, in the South of Paris.

This internship propose to investigate the dynamics of mucus clearance in the human airways, a process crucial for respiratory health and affected in diseases like cystic fibrosis. Mucus is cleared either by the coordinated beating of cilia or by cough-induced airflow, yet the role of its viscoelastic properties remains largely unexplored. As part of the CNRS-funded MUCUS project, you will study the behavior of liquid plugs formed during airway occlusion under different breathing scenarios. You can choose between two complementary axes: numerical, using the Basilisk solver and supercomputer simulations to explore how viscoelasticity affects plug rupture in 2D and 3D geometries; or experimental, using microfluidic models to visualize plug dynamics and rupture under pressure gradients and cough-like flows. This hands-on internship combines fluid mechanics, advanced simulations, and cutting-edge experiments, offering the possibility to continue into a PhD. It’s a chance to tackle real-world biomedical challenges while gaining deep expertise in complex fluid dynamics and bio-inspired flows.

This internship propose to explore the hidden world of complex fluids at the single-molecule level. Traditional rheometers only reveal bulk properties, missing the microscopic details that truly govern how polymers move and interact—especially in biologically relevant environments such as mucus transport, where long mucin chains meet arrays of cilia. Using nanopore technology and fluorescence microscopy, you’ll directly visualize individual DNA molecules as they pass through nanometer-sized channels under controlled pressure. From these experiments, you’ll uncover key insights into polymer dynamics and fluid rheology by measuring translocation times, event frequencies, and critical pressures. You’ll be actively involved in setting up and calibrating the experimental system, preparing samples, and analyzing data in both Newtonian (water, water–glycerol) and non-Newtonian (DNA, PEO) fluids.

The topic of the internship is to probe chains of semiconductor nanoplatelets by fluorescence microscopy and examine whether these structures exhibit superfluorescence, a mechanism by which incoherently excited dipoles, because of their coupling to the electromagnetic field, spontaneously develop a coherence and interfere constructively, leading to accelerated emission and original properties for emission correlations and directionality.

Diatom chains are cohesive assemblies of unicellular microorganisms that are found in still and fresh waters. Some species are passively transported by ambient currents and settle due to the weight of their dense silica shells, while others have use various strategies to move or self-propel. One species in particular, called Bacillaria Paxillifer, forms colonies of stacked rectangular cells that slide along each other while remaining parallel. Their intriguing coordinated motion, leads to beautiful and nontrivial trajectories at the scale of the colony. However, the effect of gravity and externat ambient flows on the dynamics of diatom chains must be investigated to understand the behavior of plankton and marine snow aggregates as they sink, and capture CO2, to the ocean depths.

Research overview Materials can be studied using computer simulation which enables one to probe the motion of each constituent atoms and to build correlations between the macroscopic properties and the microscopic behaviors. On the one hand, traditional quantum mechanics methods provides particularly accurate results up to the electronic structure of the material. Yet, the drawback of this method concerns its computational cost which prevents from studying large system sizes and long time scales. On the other hand, effective potentials have been developed to mimic atomic interactions thereby reducing those issues. However, these potentials are often built to reproduce bulk properties of the materials and can hardly be employed to study some specific systems including interfaces and nanomaterials. In this context, a new class of interatomic potentials based on machine-learning algorithms is being developed to retain the accuracy of traditional quantum mechanics methods while being able to run simulations with larger system sizes and longer time scales. Simulation project Using computer simulations, the student will construct a database that should be representative of the different interactions occurring in a specific material. Machine-learning potentials based on the least-angle regression algorithm as well as neural network potentials will be trained and their accuracy will be studied as a function of the size and the complexity of the database.

Research overview The formation of a crystal is triggered by the emergence of a nucleation core. Classical nucleation theory (CNT) is widely employed to discuss its nature and its origin. In CNT, the thermodynamically stable phase is always the one that grows first and its size is then driven by the free energy competition between how much it costs to build a liquid-crystal interface and the gain from growing the crystal. Yet, following Ostwald’s rule, another structure may emerge beforehand if it is closer in free energy to the mother phase. Then, structural and also chemical reorganizations happen during the growth. This multi-stage nucleation mechanism already appears in bulk systems but can be amplified in nanocrystal nucleation where surface effects and chemical reactivity are enhanced. For nanoscience to be inspired by the practical applications instead of still being driven by the synthesis possibilities, it is crucial to reach a better understanding of the unique crystallization mechanisms leading to nanocrystals. Simulation project Atomistic simulations will be performed to study crystallization of binary particles. Examples will be taken from well-studied materials including CuZr, NiAl, NaCl, Water... We will investigate the correlation between the thermodynamic conditions and the final nanoparticles. The goal is to ultimately better understand how nucleation theory is affected by downsizing to the nanometric scale.