The Centre of Mathematical Morphology (CMM) hosts French or foreign PhD students and trainees. They work on image processing, and on mathematical morphology more particularly. Application scopes are numerous: multimedia, electronics, materials science, industrial control and medicine. Most studies carried out at the CMM are linked both to theory and practice: the first element is based on the constant development of Mathematical Morphology, and the second one takes shape through industrial agreements, which give a precise aim to these studies.
The environment, at the Fontainebleau location of MINES ParisTech, is pleasant. One hour's drive from Paris, 400 researchers work on this spot. And Fontainebleau inhabitants can take advantage from one of the greatest French forests!
PhD positions
As an introduction, we will list some general features for all PhD positions at the CMM.Carrying out a PhD subject usually requires three years.
Foreign applicants should preferably master French. If not, they must be ready to follow intensive courses from their arrival. The final thesis must be written in French.
Each subject proposal shortly describes the research to be carried out, and includes the name of the person to be contacted to get more information or to apply to.
- Segmentation and classification of road scenes by analysis of LIDAR data, color and multi-spectral images
- Mathematical Morphology on Elevation maps: application to the characterization of catalyst supports and active phases
- Filtrage de la fausse alarme par des techniques modernes des réseaux de neurones artificiels
- PhD thesis: stochastic geometry for deep learning
- Detection of anomalies using Deep Learning Methods
- Causal Spatio-temporal Mathematical Morphology: Theory and Algorithms
- PhD thesis on morphological neural networks
- Séparation non-linéaire de sources hyperspectrales pour le contrôle optique en ligne
- Mathematical Morphology on Elevation maps: application to the characterization of catalyst supports and active phases
Master internships
- Improving generalization of deep learning segmentation in biomedical applications
- Détermination de la morphologie interne d’un matériau composite à matrice organique : ex-traction du tissage 3D à l’échelle mésoscopique
- Counting objects in biomedical images
- Link between skin’s microstructure and mechanical properties using quantitative image analysis on multiphoton images
- Détermination de la morphologie interne d’un matériau composite à matrice organique : ex-traction du tissage 3D à l’échelle mésoscopique