Join us

The Center for Mathematical Morphology (CMM) welcomes French and foreign doctoral students and interns. The research conducted at CMM is related to image processing in general and to mathematical morphology in particular. The fields of application are multiple: multimedia, electronics, materials sciences, industrial control and medicine. Theory and practice are the two guiding axes of most of the work carried out at the CMM:

• The theoretical axis corresponds to the ongoing development of mathematical morphology
• the practical axis is materialized through industrial collaborations which give a precise application to theses and internships.

The working environment within the site of Mines ParisTech in Fontainebleau is pleasant. One hour from Paris, the site brings together around 400 researchers. And those who live there have at their disposal one of the most beautiful forests in France!

Openings

• We don't have any open researcher position at the moment. You can find below our PhD and internships offers.

PhD Thesis Proposals

The duration of a PhD thesis is three years; foreign applicants should preferably speak French. If this is not the case, they must be ready to follow an intensive language course upon arrival.

Each thesis proposal is accompanied by a brief description of the research work to be carried out, and indicates the person to contact for more information or to submit your application.

• 22/01/2022 - Transfer learning in biomechanics dirigée par Etienne Decencière
• 20/01/2022 - Caractérisation de l’endommagement de matériaux énergétiques à l’échelle microstructurale - dirigée par François Willot et Petr Dokladal
• 20/05/2021 - Exploration des propriétés mécaniques à rupture par apprentissage automatique et microstructures virtuelles - dirigée par François Willot et Henry Proudhon
• 27/09/2021 - AI-based damage nucleation models assessed on big 4D data and micromechanical finite element simulations - dirigée par François Willot et Thilo Morgeneyer
• 10/12/2021 - Physics-informed Deep Learning for the Understanding of Mesostructure Effects on the Mechanical Failure of Reinforced Polymers - dirigée par Petr Dokladal et Etienne Decencière

Internships proposals

• 20/01/2022 - Accounting for topological and geometrical constraints in the definition of latent spaces for generative image models - Supervisors: Samy Blusseau, Etienne Decencière and François Willot
• 20/05/2021 - Implémentation GPU d’un outil de résolution d’EDPs sur des images - Encadré par François Willot
• 20/05/2021 - Simulation de la réponse mécanique d’un matériau de type soil mixingà l’aide de l’analyse d’image et d’un modèle de comportement de matériau traité - Encadré par François Willot
• 20/05/2021 - Modélisation mécanique des matériaux amorphes:De l'échelle atomique à la description continue - Encadré par François Willot
• 27/09/2021 - Exploration of damage nucleation models using graph-based deep-learning methods - Encadré par François Willot
• 13/10/2021 - Physics-Informed Artificial Intelligence for Control by Thermography - Encadré par P. Dokladal et F. WillotWillot