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Offer 1 (MSME / LISSI): R&D Data Science Master internship

5 Octobre 2021

Catégorie : Stagiaire

R&D Data Science Master internship



Osteotomies are surgical procedures that involve cutting bone / cartilage tissue with a bone chisel, or osteotome, and a mallet. They are a key part of rhinoplasty where they are used to reshape the architecture of the nose. The importance of a cosmetic result prevents surgeons from using an open, scarring approach, so they have to work blindly. Thus, they are guided only by the sound produced by the impacts of the mallet and their proprioception, thus making the procedure extremely delicate.

The Modélisation et Simulation Multi-Echelle (MSME) laboratory at CNRS has developed a new method for measuring biomechanical properties during osteotomy in rhinoplasty. This innovative method uses a surgical mallet instrumented with a piezoelectric sensor to extract information on tissue in contact with the tip of the osteotome. This approach makes it possible to estimate the stiffness of the tissues and the presence of potential fractures, thus allowing the practitioner to better guide himself during the procedure. An instrumented mallet prototype has been designed and this method has been the subject of proof of concept in vitro and on anatomical subjects.

Within the Biomechanics team of the MSME laboratory, we are offering a data science research internship to improve the existing prototype in order to adapt the technology for future clinical trials. This work will be carried out in close collaboration with the orthopedic service of Henri Mondor hospital, the laboratory Images, Signaux et Systèmes Intelligents (LISSI) andthe start-up WaveImplant specializing in the development of medical devices supporting decision-making to assist surgeons during the operation.



We are seeking bright and highly motivated master students in France, who can work in the field of artificial intelligence. The project will develop innovative deep learning approaches for computer-aided diagnosis tools for plastic surgeons. An innovative deep learning-based approach will be proposed. More details about the project will be given during the interview for confidentiality reasons.

The selected candidate will have the chance to work in an interdisciplinary team. This internship can lead to a permanent contract or PhD scholarship.



  • The candidate must be an M2 Master student or in 5th year of an engineering school in France.
  • Has done M1 in computer science, applied mathematics or electrical engineering, with a focus on machine learning.
  • Experience in Deep learning and data analysis.
  • Experience in signal and image processing.
  • Demonstrated record of high-performance programming skills in python.
  • Demonstrated analytical, verbal, and scientific writing skills in English.



Internship duration will be 6 months starting from January 2022 at an early date to start. The latest date to start the internship will be March 2022.


Location: Université Paris-Est Créteil, (Créteil, ligne 8, RER D)

Laboratory: Modélisation et Simulation Multi-Echelle (MSME), UMR mixte CNRS & Laboratoire Images, Signaux et Systèmes Intelligents (LISSI)



Please send your CV + transcripts + cover letter + recommendation letters to and (before October 30, 2021).


When submitting

  • Thanks for mentioning “Master Internship candidature Offer 1: MSME” in the object of your mail
  • If you are interested and applying to several offers with Dr. Alice OTHMANI, precise your order of preference in the text of your mail and in the object for example “Master Internship candidature Offer 1: MSME, offer2: POWDER, Offer3: Malaysia”.



1. Hubert, A., Bosc, R. and Haïat, G. “Using an impact hammer to estimate elastic modulus and thickness of a sample during an osteotomy” J Biomech Eng 142(7) (2020), pp. 071009

2. Lamassoure, L, Giunta, J, Rosi, G, Poudrel, AS, Bosc, R, and Haiat G “Use of an instrumented hammer as a decision support system during rhinoplasty: validation on an animal model”, Computer Methods in Biomechanics and Biomedical Engineering 23(S1) (2020), pp. S162-S163

3. Lamassoure, L, Giunta, J, Rosi, G, Poudrel, AS, Bosc, R, and Haiat G “Using an Impact Hammer to Perform Biomechanical Measurements during Osteotomies: Study of an Animal Model”, Proc Inst Mech Eng H 235(7) (2021), pp. 838-845.

4. Lamassoure, L, Giunta, J, Rosi, G, Poudrel, AS, Meningaud, JP, Bosc, R, and Haiat G “Anatomical subject validation of an instrumented hammer using machine learning for the classification of osteotomy fracture in rhinoplasty”, in press to Med Eng Phys

5. Giunta, J, Lamassoure, L, Rosi, G, Poudrel, AS, Meningaud, JP, Haiat G and Bosc, R, “Validation of an Instrumented Hammer for Rhinoplasty Osteotomies: A Cadaveric Study”, in press to Facial Plastic Surgery & Aesthetic Medicine